This is a partial publication list. Please find the complete list in my cv.
Selected Publications
Automated NegotiationMultiagent Systems
Mohammad, Y. (2026). Automated Negotiation with No Information about Partner Utility
Functions Using the Tentative Acceptance Unique Offers Protocol. Autonomous Agents and Multi-Agent Systems, 40(1), 21.
@article{mohammad2026tau,
keywords = {journal, important},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser},
title = {Automated Negotiation with No Information about Partner Utility
Functions Using the Tentative Acceptance Unique Offers Protocol},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {40},
number = {1},
pages = {21},
year = {2026},
doi = {10.1007/s10458-026-09745-9}
}
With the widespread adoption of AI in industrial and business
operations, finding methods to reach agreement between
intelligent agents representing self-interested entities in a
general-sum environment (cooperation within competition) is
attracting more interest in the research community. Negotiation
is a common process for reaching agreements between people and
human institutions. Automated negotiation is thus being
considered for cooperation within competition situations
involving AIs. The most widely used protocols for automated
negotiation are the Stacked Alternating Offers Protocol (SAOP)
for multilateral negotiations and the Alternating Offers Protocol
(AOP) for bilateral negotiations which directly model bargaining
as in human marketplaces. Several strategies have been proposed
for these protocols over the years. In this paper, we propose a
modification of the AOP and SAOP protocols and a method for
adapting negotiation strategies to the new protocol. We show
empirically that the proposed approach leads to higher expected
advantage for all agents, and achieves higher agreement rate,
higher welfare, and fairer agreements faster. This is achieved at
the expense of a small increase in information revelation.
Automated Negotiation
Mohammad, Y. (2023). Optimal Time-Based Strategy for Automated Negotiation. Applied Intelligence, 53(6), 6710–6735.
@article{mohammad2023optimaltime,
keywords = {journal, important},
focus = {negotiation},
author = {Mohammad, Yasser},
title = {Optimal Time-Based Strategy for Automated Negotiation},
journal = {Applied Intelligence},
volume = {53},
number = {6},
pages = {6710--6735},
year = {2023},
doi = {10.1007/s10489-022-03662-6}
}
Recent years are showing increased adoption of AI technology to
automate business and production processes thanks to the recent
successes of machine learning techniques. This leads to increased
interest in automated negotiation as a method for achieving
win-win agreements among self-interested agents. Research in
automated negotiation can be traced back to the Nash bargaining
game in the mid 20 th century. Nevertheless, finding an optimal
negotiation strategy against an unknown opponent with an unknown
utility function is still an open area of research. The most
recent result in this area is the Greedy Concession Algorithm
(GCA) which can be shown to be optimal under specific constraints
on both the negotiation protocol (non-repeating offers), opponent
(static acceptance-model) and search space (deterministic
time-based strategies). In this paper, we extend this line of
work by providing an algorithmically faster version of GCA called
Quick GCA which reduces the time-complexity of the search process
from O( 2 ) to O( ) where is the size of the outcome-space and is
the number of negotiation rounds allowed. Moreover, we show that
GCA/QGCA can be applied in a more general setting; Namely with
repeating-offers protocols and to search the more general
probabilistic time-based strategies. Finally, we heuristically
extend QGCA to more general opponents with general time-dependent
acceptance-model and negotiation settings (real-time limited
negotiations) in three steps called , , and that iteratively and
greedily modify the policy proposed by applied to an approximate
static acceptance model . The paper evaluates the proposed
approach empirically against state of the art negotiation
strategies (winners of all relevant ANAC competition winners) and
shows that it outperforms them in a wide variety of negotiation
scenarios.
Automated Negotiation
Mohammad, Y., & Nakadai, S. (2022). Concurrent Negotiations with Global Utility Functions. Proceedings of the 21st International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2022), 1947–1949. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1947.pdf
@inproceedings{mohammad2022concurrentglobal,
keywords = {conference, important},
focus = {negotiation},
author = {Mohammad, Yasser and Nakadai, Shinji},
title = {Concurrent Negotiations with Global Utility Functions},
booktitle = {Proceedings of the 21st International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2022)},
pages = {1947--1949},
publisher = {IFAAMAS},
address = {Auckland, New Zealand},
year = {2022},
doi = {10.5555/3535850.3536162},
url = {https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1947.pdf}
}
Automated Negotiation is attracting more attention from
researchers recently as it is becoming more relevant to
industrial and business applications with increased reliance on
automated systems. Most research in this area assumes either a
single negotiation thread with a well-defined utility function
for each agent involved or a set of concurrent negotiations with
an ordering of outcomes in each local negotiation. In this paper,
we consider an agent engaged in a set of concurrent negotiations
with a utility function defined only for the in of them and no
locally defined ordering of outcomes in any negotiation
independent from what happens in the others. We argue that this
problem setting is interesting both from the academic and the
industrial points of view. The paper then presents an algorithm
that allows such agent to maximize its expected global utility by
orchestrating its behavior in all negotiation threads. The
performance of the proposed method is analyzed theoretically and
empirically using simulation.
Automated Negotiation
Sengupta, A., Mohammad, Y., & Nakadai, S. (2021). An Autonomous Negotiating Agent Framework with Reinforcement
Learning Based Strategies and Adaptive Strategy Switching Mechanism. Proceedings of the 20th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2021), 1163–1172. https://ifaamas.org/Proceedings/aamas2021/pdfs/p1163.pdf
@inproceedings{sengupta2021autonomous,
keywords = {conference, important},
focus = {negotiation,machinelearning},
author = {Sengupta, Ayan and Mohammad, Yasser and Nakadai, Shinji},
title = {An Autonomous Negotiating Agent Framework with Reinforcement
Learning Based Strategies and Adaptive Strategy Switching Mechanism},
booktitle = {Proceedings of the 20th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2021)},
pages = {1163--1172},
publisher = {ACM},
year = {2021},
doi = {10.5555/3463952.3464087},
url = {https://ifaamas.org/Proceedings/aamas2021/pdfs/p1163.pdf}
}
Despite abundant negotiation strategies in literature, the
complexity of automated negotiation forbids a single strategy
from being dominant against all others in different negotiation
scenarios. To overcome this, one approach is to use mixture of
experts, but at the same time one problem of this method is the
selection of experts, as this approach is limited by the
competency of the experts selected. Another problem with most
negotiation strategies is their incapability of adapting to
dynamic variation of the opponent’s behaviour within a single
negotiation session resulting in poor performance. This work
focuses on both, solving the problem of expert selection and
adapting to the opponent’s behaviour with our Autonomous
Negotiating Agent Framework. This framework allows real-time
classification of opponent’s behaviour and provides a mechanism
to select, switch or combine strategies within a single
negotiation session. Additionally, our framework has a reviewer
component which enables self-enhancement capability by deciding
to include new strategies or replace old ones with better
strategies periodically. We demonstrate an instance of our
framework by implementing maximum entropy reinforcement learning
based strategies with a deep learning based opponent classifier.
Finally, we evaluate the performance of our agent against
state-of-the-art negotiators under varied negotiation scenarios.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Learning Interaction Protocols Using Augmented Bayesian Networks
Applied to Guided Navigation. IEEE/RSJ International Conference on Intelligent Robots And
Systems (IROS 2010), 4119–4126.
@inproceedings{mohammad2010augmentedbayes,
keywords = {conference, important},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Interaction Protocols Using Augmented Bayesian Networks
Applied to Guided Navigation},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2010)},
pages = {4119--4126},
publisher = {IEEE},
address = {Taipei, Taiwan},
year = {2010},
doi = {10.1109/IROS.2010.5651719}
}
Research in robot navigation usually concentrates on
implementing navigation algorithms that allow the robot to
navigate without human aid. In many real world situations, it is
desirable that the robot is able to understand natural gestures
from its user or partner and use this understanding to guide its
navigation. Some algorithms already exist for learning natural
gestures and/or their associated actions but most of these
systems does not allow the robot to automatically generate the
associated controller that allows it to actually navigate in the
real environment. Furthermore, a technique is needed to combine
the gestures/actions learned from interacting with multiple users
or partners. This paper resolves these two issues and provides a
complete system that allows the robot to learn interaction
protocols and act upon them using only unsupervised learning
techniques and enables it to combine the protocols learned from
multiple users/partners. The proposed approach is general and can
be applied to other interactive tasks as well. This paper also
provides a real world experiment involving 18 subjects and 72
sessions that supports the ability of the proposed system to
learn the needed gestures and to improve its knowledge of
different gestures and their associations to actions over time.
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2010). Mining Causal Relationships in Multidimensional Time Series. In E. Szczerbicki & N. T. Nguyen (Eds.), Smart Information and Knowledge Management: Advances,
Challenges, and Critical Issues (Vol. 260, pp. 309–338). Springer.
@incollection{mohammad2010miningcausal,
keywords = {bookchapter, important},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Mining Causal Relationships in Multidimensional Time Series},
booktitle = {Smart Information and Knowledge Management: Advances,
Challenges, and Critical Issues},
editor = {Szczerbicki, Edward and Nguyen, Ngoc Thanh},
series = {Studies in Computational Intelligence},
volume = {260},
pages = {309--338},
publisher = {Springer},
year = {2010},
doi = {10.1007/978-3-642-04584-4_14}
}
Time series are ubiquitous in all domains of human endeavor.
They are generated, stored, and manipulated during any kind of
activity. The goal of this chapter is to introduce a novel
approach to mine multidimensional time-series data for causal
relationships. The main feature of the proposed system is
supporting discovery of causal relations based on automatically
discovered recurring patterns in the input time series. This is
achieved by integrating a variety of data mining techniques. The
main insight of the proposed system is that causal relations can
be found more easily and robustly by analyzing meaningful events
in the time series rather than by analyzing the time series
numerical values directly. The RSST (Robust Singular Spectrum
Transform) algorithm is used to find interesting points in every
time series that is further analyzed by a constrained motif
discovery algorithm (if needed) to learn basic events of the time
series. The Granger-causality test is extended and applied to the
multidimensional time-series describing the occurrences of these
basic events rather than to the raw time-series data. The
combined algorithm is evaluated using both synthetic and real
world data. The real world application is to mine records of
activities during a human-robot interaction experiment in which a
human subject is guiding a robot to navigate using free hand
gesture. The results show that the combined system can provide
causality graphs representing the underlying relations between
the human’s actions and robot behavior that cannot be recovered
using standard causal graph learning procedures. Mining Time
Series, Robust Singular Spectrum Transform, Granger-Causality,
Mining Causal Relations
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2009). Constrained Motif Discovery in Time Series. New Generation Computing, 27(4), 319–346.
@article{mohammad2009constrainedmotif,
keywords = {journal, important},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Constrained Motif Discovery in Time Series},
journal = {New Generation Computing},
volume = {27},
number = {4},
pages = {319--346},
year = {2009},
doi = {10.1007/s00354-009-0068-x}
}
The goal of motif discovery algorithms is to efficiently find
unknown recurring patterns. In this paper we focus on motif
discovery in time series. Most available algorithms cannot
utilize domain knowledge in any way which results in quadratic or
at least super-linear time and space complexity. In this paper we
define the Constrained Motif Discovery problem which enables
utilization of domain knowledge into the motif discovery process.
The paper then provides two algorithms called MCFull and MCInc
for efficiently solving the constrained motif discovery problem.
We also show that most unconstrained motif discovery problems be
converted into constrained ones using a change-point detection
algorithm. A novel change-point detection algorithm called the
Robust Singular Spectrum Transform (RSST) is then introduced and
compared to traditional Singular Spectrum Transform using
synthetic and real-world data sets. The results show that RSST
achieves higher specificity and is more adequate for finding
constraints to convert unconstrained motif discovery problems to
constrained ones that can be solved using MCFull and MCInc. We
then compare the combination of RSST and MCFull or MCInc with two
state-of-the-art motif discovery algorithms on a large set of
synthetic time series. The results show that the proposed
algorithms provided four to ten folds increase in speed compared
the unconstrained motif discovery algorithms studied without any
loss of accuracy. RSST+MCFull is then used in a real world
human-robot interaction experiment to enable the robot to learn
free hand gestures, actions, and their associations by humans and
other robots interacting.
Robotics & HRI
Mohammad, Y. F. O., Nishida, T., & Okada, S. (2009). Unsupervised Simultaneous Learning of Gestures, Actions and Their
Associations for Human-Robot Interaction. IEEE/RSJ International Conference on Intelligent Robots And
Systems (IROS 2009), 2537–2544.
@inproceedings{mohammad2009simultaneous,
keywords = {conference, important},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki and Okada, Shogo},
title = {Unsupervised Simultaneous Learning of Gestures, Actions and Their
Associations for Human-Robot Interaction},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2009)},
pages = {2537--2544},
publisher = {IEEE},
address = {St. Louis, MO, USA},
year = {2009},
doi = {10.1109/IROS.2009.5353987}
}
To enable free natural communication between a human operator
and a robot three problems must be faced: Firstly the robot have
to know the actions it can do in the world. Secondly the robot
must be able to learn the patterns in the perceived behavior of
its operator that correspond to commands. Finally the robot needs
to know when to execute a specific action based on its perception
of the operator’s behavior. In this paper we are interested in
free hand gestures as the commanding channel. The most
restrictive solution to the aforementioned three problems is to
fix the action space (pre-programmed actions), fix the command
space (predefined gestures), and fix action-command relation
(fixed gesture meanings).Learning by demonstration can be viewed
as a technique to relax the first restriction by learning the
action space. Gesture interpretation can be viewed as a technique
to relax the second restriction by learning the command space.
Reinforcement learning can be viewed as a technique for relaxing
the third restriction by learning action-command associations
(policy). In this paper we propose a novel technique that allows
the robot to solve these three problems together learning the
action space, the command space, and their relations by just
another robot operated by a human operator. The main technical
contribution of this paper is the introduction of a novel
algorithm that allows the robot to segment and discover patterns
in its perceived signals without any prior knowledge of the
number of different patterns, their occurrences or lengths. The
second contribution is using a Ganger-Causality based test to
limit the search space for actions and commands utilizing their
relations and taking into account the autonomy level of the
robot. The paper also presents a feasibility study in which the
learning robot was able to predict actor’s behavior with 95.2
after monitoring a single interaction between a novice operator
and a WOZ operated robot representing the actor.
Books
Robotics & HRI
Mohammad, Y., & Nishida, T. (2015). Data Mining for Social Robotics: Toward Autonomously Social Robots. Springer.
@book{mohammad2015dataminingbook,
keywords = {book},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Data Mining for Social Robotics: Toward Autonomously Social Robots},
series = {Advanced Information and Knowledge Processing},
publisher = {Springer},
year = {2015},
doi = {10.1007/978-3-319-25232-2},
isbn = {978-3-319-25230-8}
}
Robotics & HRI
Nishida, T., Nakazawa, A., Ohmoto, Y., & Mohammad, Y. (2014). Conversational Informatics: A Data-Intensive Approach with Emphasis
on Nonverbal Communication. Springer.
@book{nishida2014conversational,
keywords = {book},
focus = {robotics},
author = {Nishida, Toyoaki and Nakazawa, Atsushi and Ohmoto, Yoshimasa and Mohammad, Yasser},
title = {Conversational Informatics: A Data-Intensive Approach with Emphasis
on Nonverbal Communication},
publisher = {Springer},
year = {2014},
isbn = {978-4-431-55039-6}
}
Patents
Automated Negotiation
Mohammad, Y. F. O. (2026). Negotiation Visualization Apparatus, and Negotiation Visualization
Method. A method for visualizing automated negotiation processes to enhance
usability and a human-machine interface for negotiation support based
on that visualization. (100%).
@patent{mohammad2026visualizationpatent,
keywords = {patent},
focus = {negotiation},
author = {Mohammad, Yasser F. O.},
title = {Negotiation Visualization Apparatus, and Negotiation Visualization
Method},
number = {JP2026/0000483 (application)},
location = {JP},
note = {A method for visualizing automated negotiation processes to enhance
usability and a human-machine interface for negotiation support based
on that visualization. (100\%)},
year = {2026}
}
Automated Negotiation
Mohammad, Y. F. O. (2025). Negotiation Protocol, Method and System for Implementing. A system for adapting automated negotiation strategies for the TAU
protocol. (100%). https://www.freepatentsonline.com/y2025/0054034.html
@patent{mohammad2025taupatent,
keywords = {patent},
focus = {negotiation},
author = {Mohammad, Yasser F. O.},
title = {Negotiation Protocol, Method and System for Implementing},
number = {US 18/447,264 (application)},
location = {USA},
note = {A system for adapting automated negotiation strategies for the TAU
protocol. (100\%)},
url = {https://www.freepatentsonline.com/y2025/0054034.html},
year = {2025}
}
Automated Negotiation
Ando, T., Morinaga, S., & Mohammad, Y. F. O. (2025). Negotiation Device, Negotiation Method, and Storage Medium.
@patent{ando2025devicepatent,
keywords = {patent},
focus = {negotiation},
author = {Ando, Tomohito and Morinaga, Satoshi and Mohammad, Yasser F. O.},
title = {Negotiation Device, Negotiation Method, and Storage Medium},
number = {US 19/095,084 (application)},
location = {USA},
year = {2025}
}
Automated Negotiation
Sengupta, A., Mohammad, Y. F. O., & Nakadai, S. (2024). Automated Negotiation Agent Adaptation. Automated negotiation agent adaptation is performed by detecting
change in a utility function. (30%). https://patents.google.com/patent/US12086895B2/en
@patent{sengupta2024adaptationpatent,
keywords = {patent},
focus = {negotiation},
author = {Sengupta, Ayan and Mohammad, Yasser F. O. and Nakadai, Shinji},
title = {Automated Negotiation Agent Adaptation},
number = {US 12,086,895 (granted)},
location = {USA},
note = {Automated negotiation agent adaptation is performed by detecting
change in a utility function. (30\%)},
url = {https://patents.google.com/patent/US12086895B2/en},
year = {2024}
}
Automated Negotiation
Mohammad, Y. F. O., & Nakadai, S. (2024). Negotiation Method Including Selection of Neural Network and System
for Implementing. A system for selecting and using a neural architecture for automated
negotiation. (90%). https://www.freepatentsonline.com/y2024/0386262.html
@patent{mohammad2024nnselectionpatent,
keywords = {patent},
focus = {negotiation,machinelearning},
author = {Mohammad, Yasser F. O. and Nakadai, Shinji},
title = {Negotiation Method Including Selection of Neural Network and System
for Implementing},
number = {US 18/317,112 (application)},
location = {USA},
note = {A system for selecting and using a neural architecture for automated
negotiation. (90\%)},
url = {https://www.freepatentsonline.com/y2024/0386262.html},
year = {2024}
}
Mohammad, Y. F. O. (2023). Policy Generation Apparatus, Control Method, and Non-Transitory
Computer-Readable Storage Medium. A method for concurrent negotiation proven to be optimal against
opponents with static acceptance models. (100%). https://patents.google.com/patent/US20230289908A1/en
@patent{mohammad2023policypatent,
keywords = {patent},
author = {Mohammad, Yasser F. O.},
title = {Policy Generation Apparatus, Control Method, and Non-Transitory
Computer-Readable Storage Medium},
number = {US 18/005,912 (US20230289908A1); PCT/JP2020/029145},
location = {USA},
note = {A method for concurrent negotiation proven to be optimal against
opponents with static acceptance models. (100\%)},
url = {https://patents.google.com/patent/US20230289908A1/en},
year = {2023}
}
Automated Negotiation
Mohammad, Y. F. O., & Ninagawa, K. (2022). Negotiation Method Including Elicitation and System for
Implementing. A system for negotiation under uncertainty with
elicitation-during-negotiation support using the Value of Information
concept. (50%). https://www.freepatentsonline.com/y2022/0366483.html
@patent{mohammad2022elicitationpatent,
keywords = {patent},
focus = {negotiation},
author = {Mohammad, Yasser F. O. and Ninagawa, Kotone},
title = {Negotiation Method Including Elicitation and System for
Implementing},
number = {US 17/388,004 (application)},
location = {USA},
note = {A system for negotiation under uncertainty with
elicitation-during-negotiation support using the Value of Information
concept. (50\%)},
url = {https://www.freepatentsonline.com/y2022/0366483.html},
year = {2022}
}
Automated Negotiation
Sengupta, A., & Mohammad, Y. F. O. (2022). Adaptive Autonomous Negotiation Method and System of Using. A new automated negotiation method that can generalize along
negotiation domains and opponents. (30%). https://patents.google.com/patent/US20220108412A1/en
@patent{sengupta2022adaptivepatent,
keywords = {patent},
focus = {negotiation},
author = {Sengupta, Ayan and Mohammad, Yasser F. O.},
title = {Adaptive Autonomous Negotiation Method and System of Using},
number = {US 17/184,590 (application)},
location = {USA},
note = {A new automated negotiation method that can generalize along
negotiation domains and opponents. (30\%)},
url = {https://patents.google.com/patent/US20220108412A1/en},
year = {2022}
}
Mohammad, Y. F. O., & Hoashi, K. (2021). Learning Data Generator, Judgment Device and Program. A method for training multiple pipelines of convolutional neural
networks to achieve high accuracy in activity recognition. (90%). https://patents.google.com/patent/JP2019087106A/en
@patent{mohammad2021learningdatapatent,
keywords = {patent},
author = {Mohammad, Yasser F. O. and Hoashi, Keiichiro},
title = {Learning Data Generator, Judgment Device and Program},
number = {JP6838259B2 (granted); app. JP2019-087106},
location = {Japan},
note = {A method for training multiple pipelines of convolutional neural
networks to achieve high accuracy in activity recognition. (90\%)},
url = {https://patents.google.com/patent/JP2019087106A/en},
year = {2021}
}
Mohammad, Y. F. O., & Hoashi, K. (2020). Neural Network Regulator, Device and Program. A method for compression of neural networks (convolutional or
otherwise) based on novel application of feature selection
techniques. (90%). https://patents.google.com/patent/JP6838259B2/en
@patent{mohammad2020nnregulatorpatent,
keywords = {patent},
author = {Mohammad, Yasser F. O. and Hoashi, Keiichiro},
title = {Neural Network Regulator, Device and Program},
number = {JP6754343B2 (granted)},
location = {Japan},
note = {A method for compression of neural networks (convolutional or
otherwise) based on novel application of feature selection
techniques. (90\%)},
url = {https://patents.google.com/patent/JP6838259B2/en},
year = {2020}
}
Invited & Industrial Talks
Mohammad, Y. (2025). Advances in Solving Automated Negotiation Games. Invited talk at the Second Workshop on Game AI Algorithms
and Multi-Agent Learning (GAAMAL@IJCAI 2025), Montreal,
Canada.
@misc{mohammad2025gaiwtalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Advances in Solving Automated Negotiation Games},
howpublished = {Invited talk at the Second Workshop on Game AI Algorithms
and Multi-Agent Learning (GAAMAL@IJCAI 2025), Montreal,
Canada},
year = {2025}
}
Mohammad, Y. (2025). NEC on Catena-X — Automated Negotiation Technology for the
Catena-X Community. Talk at Digital Twin Innovation in Manufacturing, Energy Related Industries, Heilbronn, Germany.
@misc{mohammad2025catenax,
keywords = {industrialtalk},
author = {Mohammad, Yasser},
title = {{NEC} on {Catena-X} --- Automated Negotiation Technology for the {
Catena-X} Community},
howpublished = {Talk at Digital Twin Innovation in Manufacturing, Energy \&
Related Industries, Heilbronn, Germany},
year = {2025}
}
Mohammad, Y. (2024). Automated Negotiation: A New Frontier for AI in Business. Invited talk at Integration of Machine Learning and
Mathematical Modeling, and Deepening of Its Theory II, Kyushu
, Japan.
@misc{mohammad2024kyushutalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Automated Negotiation: A New Frontier for {AI} in Business},
howpublished = {Invited talk at Integration of Machine Learning and
Mathematical Modeling, and Deepening of Its Theory II, Kyushu
, Japan},
year = {2024}
}
Mohammad, Y. (2024). Digital Twin Autonomous Orchestration and Coordination. Talk at the Digital Twin Consortium Q3 Member Meeting,
Chicago, IL, USA. https://www.digitaltwinconsortium.org/events/q3-2024-member-meeting/
@misc{mohammad2024dtctalk,
keywords = {industrialtalk},
author = {Mohammad, Yasser},
title = {Digital Twin Autonomous Orchestration and Coordination},
howpublished = {Talk at the Digital Twin Consortium Q3 Member Meeting,
Chicago, IL, USA},
url = {https://www.digitaltwinconsortium.org/events/q3-2024-member-meeting/},
year = {2024}
}
Mohammad, Y. (2022). Generalized Bargaining Mechanisms: Mechanism Design for Automated
Negotiation. Invited talk at the IBM/DIMACS Workshop on Bridging Game
Theory and Machine Learning for Multi-Party Decision Making,
New Jersey, USA.
@misc{mohammad2022dimacstalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Generalized Bargaining Mechanisms: Mechanism Design for Automated
Negotiation},
howpublished = {Invited talk at the IBM/DIMACS Workshop on Bridging Game
Theory and Machine Learning for Multi-Party Decision Making,
New Jersey, USA},
year = {2022}
}
Mohammad, Y. (2022). Concurrent Negotiation in Supply Chains: Problems, Solutions and
Challenges. Invited talk at the 13th International Workshop on
Agent-Based Complex Automated Negotiations (ACAN@IJCAI 2022),
Vienna, Austria.
@misc{mohammad2022acantalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Concurrent Negotiation in Supply Chains: Problems, Solutions and
Challenges},
howpublished = {Invited talk at the 13th International Workshop on
Agent-Based Complex Automated Negotiations (ACAN@IJCAI 2022),
Vienna, Austria},
year = {2022}
}
Mohammad, Y. (2018). Analysis and Commentary on PRIANAC and ANAC. Invited talk at the Pacific Rim International Automated
Negotiation Agents Competition (PRIANAC@PRIMA 2018), Tokyo,
Japan.
@misc{mohammad2018prianactalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Analysis and Commentary on {PRIANAC} and {ANAC}},
howpublished = {Invited talk at the Pacific Rim International Automated
Negotiation Agents Competition (PRIANAC@PRIMA 2018), Tokyo,
Japan},
year = {2018}
}
Mohammad, Y. (2018). Fluid Imitation. Invited talk at Human-Robot Interaction: From Service to
Industry (HRI-SI2018@IEEE RO-MAN 2018), Nanjing, China.
@misc{mohammad2018fluidtalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {Fluid Imitation},
howpublished = {Invited talk at Human-Robot Interaction: From Service to
Industry (HRI-SI2018@IEEE RO-MAN 2018), Nanjing, China},
year = {2018}
}
Mohammad, Y. (2012). SSA Application to Motif Discovery and Causality Analysis in
Robotics. Invited talk at the Third International Conference on SSA
and Its Applications, Beijing, China.
@misc{mohammad2012ssatalk,
keywords = {talk},
author = {Mohammad, Yasser},
title = {{SSA} Application to Motif Discovery and Causality Analysis in
Robotics},
howpublished = {Invited talk at the Third International Conference on SSA
and Its Applications, Beijing, China},
year = {2012}
}
Tutorials
Mohammad, Y. (2026). Generative AI for Automated Negotiation. Tutorial at the 27th International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2026), Nagoya,
Japan.
@misc{mohammad2026primatutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Generative AI for Automated Negotiation},
howpublished = {Tutorial at the 27th International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2026), Nagoya,
Japan},
year = {2026}
}
Mohammad, Y. (2025). Developing Data-Driven Automated Negotiating Agents. Tutorial at the 29th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD 2025), Sydney, Australia.
@misc{mohammad2025pakddtutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Developing Data-Driven Automated Negotiating Agents},
howpublished = {Tutorial at the 29th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD 2025), Sydney, Australia},
year = {2025}
}
Mohammad, Y. (2025). Reinforcement Learning for Automated Negotiation. Tutorial at the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025), Detroit, MI, USA.
@misc{mohammad2025aamastutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Reinforcement Learning for Automated Negotiation},
howpublished = {Tutorial at the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025), Detroit, MI, USA},
year = {2025}
}
Mohammad, Y. (2023). Reinforcement Learning for Automated Negotiation. Tutorial at the Australasian Joint Conference on Artificial
Intelligence (AJCAI 2023), Brisbane, Australia.
@misc{mohammad2023ajcaitutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Reinforcement Learning for Automated Negotiation},
howpublished = {Tutorial at the Australasian Joint Conference on Artificial
Intelligence (AJCAI 2023), Brisbane, Australia},
year = {2023}
}
Mohammad, Y. (2023). Automated Negotiation in Supply Chains. Tutorial at the IEEE International Conference on Agents (ICA
2023), Kyoto, Japan.
@misc{mohammad2023icatutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Automated Negotiation in Supply Chains},
howpublished = {Tutorial at the IEEE International Conference on Agents (ICA
2023), Kyoto, Japan},
year = {2023}
}
Mohammad, Y., & Greenwald, A. (2022). Automated Negotiation: Challenges and Tools. Tutorial at the 36th AAAI Conference on Artificial
Intelligence (AAAI 2022), Vancouver, Canada.
@misc{mohammad2022aaaitutorial,
keywords = {tutorial},
author = {Mohammad, Yasser and Greenwald, Amy},
title = {Automated Negotiation: Challenges and Tools},
howpublished = {Tutorial at the 36th AAAI Conference on Artificial
Intelligence (AAAI 2022), Vancouver, Canada},
year = {2022}
}
Mohammad, Y. (2020). Automated Negotiation in Supply Chain Management. Tutorial at the 23rd International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2020), Nagoya,
Japan.
@misc{mohammad2020primatutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Automated Negotiation in Supply Chain Management},
howpublished = {Tutorial at the 23rd International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2020), Nagoya,
Japan},
year = {2020}
}
Mohammad, Y. (2019). Automated Negotiation: Challenges and Tools. Tutorial at the 22nd International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2019), Turin,
Italy.
@misc{mohammad2019primatutorial,
keywords = {tutorial},
author = {Mohammad, Yasser},
title = {Automated Negotiation: Challenges and Tools},
howpublished = {Tutorial at the 22nd International Conference on Principles
and Practice of Multi-Agent Systems (PRIMA 2019), Turin,
Italy},
year = {2019}
}
Journal Articles
Automated NegotiationMultiagent Systems
Mohammad, Y. (2026). Automated Negotiation with No Information about Partner Utility
Functions Using the Tentative Acceptance Unique Offers Protocol. Autonomous Agents and Multi-Agent Systems, 40(1), 21.
@article{mohammad2026tau,
keywords = {journal, important},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser},
title = {Automated Negotiation with No Information about Partner Utility
Functions Using the Tentative Acceptance Unique Offers Protocol},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {40},
number = {1},
pages = {21},
year = {2026},
doi = {10.1007/s10458-026-09745-9}
}
With the widespread adoption of AI in industrial and business
operations, finding methods to reach agreement between
intelligent agents representing self-interested entities in a
general-sum environment (cooperation within competition) is
attracting more interest in the research community. Negotiation
is a common process for reaching agreements between people and
human institutions. Automated negotiation is thus being
considered for cooperation within competition situations
involving AIs. The most widely used protocols for automated
negotiation are the Stacked Alternating Offers Protocol (SAOP)
for multilateral negotiations and the Alternating Offers Protocol
(AOP) for bilateral negotiations which directly model bargaining
as in human marketplaces. Several strategies have been proposed
for these protocols over the years. In this paper, we propose a
modification of the AOP and SAOP protocols and a method for
adapting negotiation strategies to the new protocol. We show
empirically that the proposed approach leads to higher expected
advantage for all agents, and achieves higher agreement rate,
higher welfare, and fairer agreements faster. This is achieved at
the expense of a small increase in information revelation.
Automated NegotiationDigital Twins
Mohammad, Y., Chen, H., Higa, R., Ando, T., & Morinaga, S. (2025). Generative AI for Automated Negotiation. Journal of Innovation.
@article{mohammad2025generativeai,
keywords = {journal},
focus = {negotiation,digitaltwin},
author = {Mohammad, Yasser and Chen, Haifeng and Higa, Ryota and Ando, Tomohito and Morinaga, Satoshi},
title = {Generative {AI} for Automated Negotiation},
journal = {Journal of Innovation},
year = {2025},
month = dec
}
ML & Time-Series
Petch, L., Moustafa, A., Ma, X., & Mohammad, Y. (2025). HFL-GAN: Scalable Hierarchical Federated Learning GAN for High
Quantity Heterogeneous Clients. Applied Intelligence, 55, 170.
@article{petch2025hflgan,
keywords = {journal},
focus = {timeseries,machinelearning},
author = {Petch, Lewis and Moustafa, Ahmed and Ma, Xinhui and Mohammad, Yasser},
title = {{HFL-GAN}: Scalable Hierarchical Federated Learning {GAN} for High
Quantity Heterogeneous Clients},
journal = {Applied Intelligence},
volume = {55},
pages = {170},
year = {2025},
doi = {10.1007/s10489-024-05924-x}
}
Hamdi, F. A., Kataoka, K., Arai, Y., Takeda, N., Yamamoto, M., Mohammad, Y. F. O., Ghazy, N. A., & Suzuki, T. (2023). An Octopamine Receptor Involved in Feeding Behavior of the
Two-Spotted Spider Mite, Tetranychus urticae Koch: A Possible
Candidate for RNAi-Based Pest Control. Entomologia Generalis, 43(1).
@article{hamdi2023octopamine,
keywords = {journal},
author = {Hamdi, Faten Abdelsalam and Kataoka, Kosuke and Arai, Yuka and Takeda, Naoki and Yamamoto, Masanobu and Mohammad, Yasser F. O. and Ghazy, Noureldin Abuelfadl and Suzuki, Takeshi},
title = {An Octopamine Receptor Involved in Feeding Behavior of the
Two-Spotted Spider Mite, Tetranychus urticae {Koch}: A Possible
Candidate for {RNAi}-Based Pest Control},
journal = {Entomologia Generalis},
volume = {43},
number = {1},
year = {2023}
}
Automated Negotiation
Mohammad, Y. (2023). Optimal Time-Based Strategy for Automated Negotiation. Applied Intelligence, 53(6), 6710–6735.
@article{mohammad2023optimaltime,
keywords = {journal, important},
focus = {negotiation},
author = {Mohammad, Yasser},
title = {Optimal Time-Based Strategy for Automated Negotiation},
journal = {Applied Intelligence},
volume = {53},
number = {6},
pages = {6710--6735},
year = {2023},
doi = {10.1007/s10489-022-03662-6}
}
Recent years are showing increased adoption of AI technology to
automate business and production processes thanks to the recent
successes of machine learning techniques. This leads to increased
interest in automated negotiation as a method for achieving
win-win agreements among self-interested agents. Research in
automated negotiation can be traced back to the Nash bargaining
game in the mid 20 th century. Nevertheless, finding an optimal
negotiation strategy against an unknown opponent with an unknown
utility function is still an open area of research. The most
recent result in this area is the Greedy Concession Algorithm
(GCA) which can be shown to be optimal under specific constraints
on both the negotiation protocol (non-repeating offers), opponent
(static acceptance-model) and search space (deterministic
time-based strategies). In this paper, we extend this line of
work by providing an algorithmically faster version of GCA called
Quick GCA which reduces the time-complexity of the search process
from O( 2 ) to O( ) where is the size of the outcome-space and is
the number of negotiation rounds allowed. Moreover, we show that
GCA/QGCA can be applied in a more general setting; Namely with
repeating-offers protocols and to search the more general
probabilistic time-based strategies. Finally, we heuristically
extend QGCA to more general opponents with general time-dependent
acceptance-model and negotiation settings (real-time limited
negotiations) in three steps called , , and that iteratively and
greedily modify the policy proposed by applied to an approximate
static acceptance model . The paper evaluates the proposed
approach empirically against state of the art negotiation
strategies (winners of all relevant ANAC competition winners) and
shows that it outperforms them in a wide variety of negotiation
scenarios.
Automated Negotiation
Mohammad, Y. (2021). Concurrent Local Negotiations with a Global Utility Function: A
Greedy Approach. Autonomous Agents and Multi-Agent Systems, 35(2), 28.
@article{mohammad2021concurrentlocal,
keywords = {journal},
focus = {negotiation},
author = {Mohammad, Yasser},
title = {Concurrent Local Negotiations with a Global Utility Function: A
Greedy Approach},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {35},
number = {2},
pages = {28},
year = {2021},
doi = {10.1007/s10458-021-09512-y}
}
Automated Negotiation is a growing area of research in recent
years as it provides a mechanism for intelligent agents
representing people and institutions to coordinate their behavior
in a complex environment under rational selfish assumptions. Most
research in this area assumes either a single negotiation thread
with a well-defined utility function for each agent involved or a
set of concurrent negotiations with an ordering of outcomes in
each local negotiation. In this paper, we consider an agent
engaging in a set of concurrent negotiations with the utility
function only defined for the in of them and no locally defined
ordering of outcomes in any negotiation. The paper presents an
algorithm that allows such agent to maximize its expected global
utility function by orchestrating its behavior in all negotiation
threads. The performance of the proposed method is analyzed
theoretically and empirically using simulation in the context of
a trading market.
Automated NegotiationMultiagent Systems
Mohammad, Y., Nakadai, S., Morinaga, S., & Fujita, K. (2020). Supply Chain Management League (SCML) — Automated Negotiating
Agent Competition for Manufacturing Value Chain. Journal of the Japanese Society for Artificial Intelligence, 35(3).
@article{mohammad2020scmljournal,
keywords = {journal},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and Nakadai, Shinji and Morinaga, Satoshi and Fujita, Katsuhide},
title = {Supply Chain Management League {(SCML)} --- Automated Negotiating
Agent Competition for Manufacturing Value Chain},
journal = {Journal of the Japanese Society for Artificial Intelligence},
volume = {35},
number = {3},
year = {2020},
month = may
}
ML & Time-Series
Mohammad, Y. F. O., Matsumoto, K., & Hoashi, K. (2019). Selecting Orientation-Insensitive Features for Activity Recognition
from Accelerometers. IEICE Transactions on Information and Systems, E102-D(1), 104–115.
@article{mohammad2019selecting,
keywords = {journal},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Matsumoto, Kazunori and Hoashi, Keiichiro},
title = {Selecting Orientation-Insensitive Features for Activity Recognition
from Accelerometers},
journal = {IEICE Transactions on Information and Systems},
volume = {E102-D},
number = {1},
pages = {104--115},
year = {2019},
doi = {10.1587/transinf.2018EDP7092}
}
ML & Time-Series
Mohammad, Y. F. O., Matsumoto, K., & Hoashi, K. (2018). Primitive Activity Recognition from Short Sequences of Sensory Data
. Applied Intelligence, 48(10), 3748–3761.
@article{mohammad2018primitive,
keywords = {journal},
focus = {timeseries,machinelearning},
author = {Mohammad, Yasser F. O. and Matsumoto, Kazunori and Hoashi, Keiichiro},
title = {Primitive Activity Recognition from Short Sequences of Sensory Data
},
journal = {Applied Intelligence},
volume = {48},
number = {10},
pages = {3748--3761},
year = {2018},
doi = {10.1007/s10489-018-1166-6}
}
Activity recognition (AR) from mobile device sensors and
wearables is attracting more attention from the research
community due to the widespread adoption of these devices and the
unique opportunity they provide for understanding user’s behavior
leading to novel services and improvements in the delivery of
existing ones. Approaches to tackle this problem either rely on
predefined statistical features of sensor data streams or feature
learning with the latter providing higher accuracies in most
cases. Deep learning methods proved more effective than
traditional approaches to feature learning in multiple studies.
This paper presents a novel end-to-end trainable deep
architecture that utilizes multiple convolutional neural networks
(CNN), late fusion and extensive layer bypassing. The proposed
method can easily accommodate multiple sensors and signal
representations. The proposed approach is validated on eight
publicly available datasets using a variety of evaluation
conditions showing that it outperforms state-of-the-art methods
in six of them.
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2016). Exact Multi-Length Scale and Mean Invariant Motif Discovery. Applied Intelligence, 44(2), 322–339.
@article{mohammad2016exactmulti,
keywords = {journal},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Exact Multi-Length Scale and Mean Invariant Motif Discovery},
journal = {Applied Intelligence},
volume = {44},
number = {2},
pages = {322--339},
year = {2016},
doi = {10.1007/s10489-015-0684-8}
}
Discovering approximately recurrent motifs (ARMs) in timeseries
is an active area of research in data mining. Exact motif
discovery is defined as the problem of efficiently finding the
most similar pairs of timeseries subsequences and can be used as
a basis for discovering ARMs. The most efficient algorithm for
solving this problem was the MK algorithm which was designed to
find a single pair of timeseries subsequences with maximum
similarity at a known length. This paper provides three of
extensions of the MK algorithm that allow it to find the top K
similar subsequences at multiple lengths using both the Euclidean
distance metric and scale invariant normalized version of it. The
proposed algorithms are then applied to both synthetic data and
real-world data with a focus on discovery of ARMs in human motion
trajectories. Data Mining Motif Discovery HRI Human Behavior
Understanding Exact Motif Discovery
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2015). Why Should We Imitate Robots? Effect of Back Imitation on
Judgment of Imitative Skill. International Journal of Social Robotics, 7(4), 497–512.
@article{mohammad2015whyimitate,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Why Should We Imitate Robots? {Effect} of Back Imitation on
Judgment of Imitative Skill},
journal = {International Journal of Social Robotics},
volume = {7},
number = {4},
pages = {497--512},
year = {2015},
doi = {10.1007/s12369-015-0282-2}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2015). Learning Interaction Protocols by Mimicking: Understanding and
Reproducing Human Interactive Behavior. Pattern Recognition Letters, 66, 62–70.
@article{mohammad2015mimicking,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Interaction Protocols by Mimicking: Understanding and
Reproducing Human Interactive Behavior},
journal = {Pattern Recognition Letters},
volume = {66},
pages = {62--70},
year = {2015},
doi = {10.1016/j.patrec.2014.11.010}
}
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2015). Shift Density Estimation Based Approximately Recurring Motif
Discovery. Applied Intelligence, 42(1), 112–134.
@article{mohammad2015shiftdensity,
keywords = {journal},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Shift Density Estimation Based Approximately Recurring Motif
Discovery},
journal = {Applied Intelligence},
volume = {42},
number = {1},
pages = {112--134},
year = {2015},
doi = {10.1007/s10489-014-0531-3}
}
Approximately Recurring Motif (ARM) discovery is the problem of
finding unknown patterns that appear frequently in real valued
timeseries. In this paper, we propose a novel algorithm for
solving this problem that can achieve performance comparable with
the most accurate algorithms with a speed comparable to the
fastest ones. The main idea behind the proposed algorithm is to
convert the problem of ARM discovery into a density estimation
problem in the single dimensionality shift-space (rather than in
the original time-series space). This makes the algorithm more
robust to short noise bursts that can dramatically affect the
performance of most available algorithms. The paper also reports
the results of applying the proposed algorithm to synthetic and
three real-world datasets in the domains of gesture discovery and
motion primitive discovery. Data Mining Motif Discovery HRI
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2013). Learning Where to Look: Autonomous Development of Gaze Behavior for
Natural Human-Robot Interaction. Interaction Studies, 14(3), 419–450.
@article{mohammad2013learningwhere,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Where to Look: Autonomous Development of Gaze Behavior for
Natural Human-Robot Interaction},
journal = {Interaction Studies},
volume = {14},
number = {3},
pages = {419--450},
year = {2013}
}
Gaze is one of the most important nonverbal behaviors in
regulating human-human interactions in close encounters. Several
approaches have been proposed to design robots that can exhibit
human-like natural gazing behavior. Most of the available
techniques though are based on careful hard-coding of behavioral
rules that are usually extracted from analysis of human-human
interaction corpora or theories of gaze behavior in humans. The
major disadvantage of this approach is the difficulty in
discovering these behavioral rules specially taking into account
cultural effects, and the effect of power distribution on gaze
behavior. In this paper, we report the development and evaluation
of an unsupervised gaze controller for a listener robot that
tries to alleviate this problem of engineered gazing behavior.
The system utilizes two major learning mechanisms. First, it
learns a set of recurrent gaze patterns called basic interactive
acts using a motif discovery algorithm. A hierarchical controller
to activate these acts as needed during the interaction is then
learned resulting in a grounded controller. The system was
implemented and evaluated in comparison with a reactive gaze
controller that was previously shown to provide human-like gazing
behavior. The proposed system was shown to be superior in terms
of naturalness, human-likeness and comfort of the human partner.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2012). Fluid Imitation: Discovering What to Imitate. International Journal of Social Robotics, 4(4), 369–382.
@article{mohammad2012fluidjournal,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Fluid Imitation: Discovering What to Imitate},
journal = {International Journal of Social Robotics},
volume = {4},
number = {4},
pages = {369--382},
year = {2012},
doi = {10.1007/s12369-012-0153-z}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Using Physiological Signals to Detect Natural Interactive Behavior. Applied Intelligence, 33(1), 79–92.
@article{mohammad2010physiological,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Using Physiological Signals to Detect Natural Interactive Behavior},
journal = {Applied Intelligence},
volume = {33},
number = {1},
pages = {79--92},
year = {2010},
doi = {10.1007/s10489-010-0241-4}
}
Many researchers in the HRI and ECA domains try to build robots
and agents that exhibit human-like behavior in real-world close
encounter situations. One major requirement for comparing such
robots and agents is to have an objective quantitative metric for
measuring naturalness in various kinds of interactions. Some
researchers have already suggested techniques for measuring
stress level, awareness etc using physiological signals like GSR
and BVP. One problem of available techniques is that they are
only tested with extreme situations and cannot according to the
analysis provided in this paper distinguish the response of human
subjects in natural interaction situations. One other problem of
the available techniques is that most of them require calibration
and some times ad-hoc adjustment for every subject. This paper
explores the usefulness of various kinds of physiological signals
and statistics in distinguishing natural and unnatural partner
behavior in a close encounter situation. The paper also explores
the usefulness of these statistics in various time slots of the
interaction. Based on this analysis a regressor was designed to
measure naturalness in close encounter situations and was
evaluated using human-human and human-robot interactions and
shown to achieve 87.5 situations. RSST Psychophysiology HRI data
mining
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Controlling Gaze with an Embodied Interactive Control Architecture. Applied Intelligence, 32(2), 148–163.
@article{mohammad2010controllinggaze,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Controlling Gaze with an Embodied Interactive Control Architecture},
journal = {Applied Intelligence},
volume = {32},
number = {2},
pages = {148--163},
year = {2010},
doi = {10.1007/s10489-009-0180-0}
}
Human-Robot Interaction (HRI) is a growing field of research
that targets the development of robots which are easy to operate,
more engaging and more entertaining. Natural human-like behavior
is considered by many researchers as an important target of HRI.
Research in Human-Human communications revealed that gaze control
is one of the major interactive behaviors used by humans in close
encounters. Human-like gaze control is then one of the important
behaviors that a robot should have in order to provide natural
interactions with human partners. To develop human-like natural
gaze control that can integrate easily with other behaviors of
the robot, a flexible robotic architecture is needed. Most
robotic architectures available were developed with autonomous
robots in mind. Although robots developed for HRI are usually
autonomous, their autonomy is combined with interactivity, which
adds more challenges on the design of the robotic architectures
supporting them. This paper reports the development and
evaluation of two gaze controllers using a new cross-platform
robotic architecture for HRI applications called EICA (The
Embodied Interactive Control Architecture), that was designed to
meet those challenges emphasizing how low level attention
focusing and action integration are implemented. Evaluation of
the gaze controllers revealed human-like behavior in terms of
mutual attention, gaze toward partner, and mutual gaze. The paper
also reports a novel Floating Point Genetic Algorithm (FPGA) for
learning the parameters of various processes of the gaze
controller. Robotic Architectures Action Integration HRI Gaze
Control
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2009). Constrained Motif Discovery in Time Series. New Generation Computing, 27(4), 319–346.
@article{mohammad2009constrainedmotif,
keywords = {journal, important},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Constrained Motif Discovery in Time Series},
journal = {New Generation Computing},
volume = {27},
number = {4},
pages = {319--346},
year = {2009},
doi = {10.1007/s00354-009-0068-x}
}
The goal of motif discovery algorithms is to efficiently find
unknown recurring patterns. In this paper we focus on motif
discovery in time series. Most available algorithms cannot
utilize domain knowledge in any way which results in quadratic or
at least super-linear time and space complexity. In this paper we
define the Constrained Motif Discovery problem which enables
utilization of domain knowledge into the motif discovery process.
The paper then provides two algorithms called MCFull and MCInc
for efficiently solving the constrained motif discovery problem.
We also show that most unconstrained motif discovery problems be
converted into constrained ones using a change-point detection
algorithm. A novel change-point detection algorithm called the
Robust Singular Spectrum Transform (RSST) is then introduced and
compared to traditional Singular Spectrum Transform using
synthetic and real-world data sets. The results show that RSST
achieves higher specificity and is more adequate for finding
constraints to convert unconstrained motif discovery problems to
constrained ones that can be solved using MCFull and MCInc. We
then compare the combination of RSST and MCFull or MCInc with two
state-of-the-art motif discovery algorithms on a large set of
synthetic time series. The results show that the proposed
algorithms provided four to ten folds increase in speed compared
the unconstrained motif discovery algorithms studied without any
loss of accuracy. RSST+MCFull is then used in a real world
human-robot interaction experiment to enable the robot to learn
free hand gestures, actions, and their associations by humans and
other robots interacting.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2009). Toward Combining Autonomy and Interactivity for Social Robots. AI & Society, 24(1), 35–49.
@article{mohammad2009combining,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Toward Combining Autonomy and Interactivity for Social Robots},
journal = {AI \& Society},
volume = {24},
number = {1},
pages = {35--49},
year = {2009},
doi = {10.1007/s00146-009-0196-3}
}
Mohammad, Y. F. O., & Nishida, T. (2009). Interactive Perception for Amplification of Intended Behavior in
Complex Noisy Environments. AI & Society, 23(2), 167–186.
@article{mohammad2009interactiveperception,
keywords = {journal},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Interactive Perception for Amplification of Intended Behavior in
Complex Noisy Environments},
journal = {AI \& Society},
volume = {23},
number = {2},
pages = {167--186},
year = {2009},
doi = {10.1007/s00146-007-0137-y}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2008). Interaction Between Untrained Users and a Miniature Robot in a
Collaborative Navigation Controlled Experiment. International Journal of Information Acquisition, 5(4), 291–307.
@article{mohammad2008untrained,
keywords = {journal},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Interaction Between Untrained Users and a Miniature Robot in a
Collaborative Navigation Controlled Experiment},
journal = {International Journal of Information Acquisition},
volume = {5},
number = {4},
pages = {291--307},
year = {2008},
doi = {10.1142/S0219878908001727}
}
Future robots are expected to be operated by untrained persons
using natural means of communication. This entails the necessity
of understanding how humans will communicate with such robots
especially in the non humanoid case where anthropomorphism is in
its minimum. This paper presents a controlled experiment to study
the interaction between untrained human users and a miniature
robot in a collaborative navigation task. Three dimensions of the
interaction are studied: 1. How the human operators tended to use
gestures during their interaction. Interesting results about the
patterns of gesture use are reported. 2. Signs of human
adaptation to the task requirements and robot capabilities. Three
main findings about the human adaptation are reported. 3. The
effectiveness and naturalness of using motion cues as a feedback
mechanism from the robot in comparison with verbal feedback. The
results of the experiment showed that there is no significant
difference in the task completion accuracy and time or in the
feeling of naturalness between motion cues and verbal feedback,
and there is a statistically significant improvement when using
either of them compared with the control case. Moreover the
subjects selected the motion cues feedback mechanism more
frequently as the preferred feedback modality for them.
Robotics & HRI
Nishida, T., Terada, K., Tajima, T., Hatakeyama, M., Ogasawara, Y., Sumi, Y., Xu, Y., Mohammad, Y. F. O., Tarasenko, K., Ohya, T., & Hiramatsu, T. (2006). Toward Robots as Embodied Knowledge Media. IEICE Transactions on Information and Systems, E89-D(6), 1768–1780.
@article{nishida2006embodiedknowledge,
keywords = {journal},
focus = {robotics},
author = {Nishida, Toyoaki and Terada, Kazunori and Tajima, Takashi and Hatakeyama, Makoto and Ogasawara, Yoshiyasu and Sumi, Yasuyuki and Xu, Yong and Mohammad, Yasser F. O. and Tarasenko, Kateryna and Ohya, Taku and Hiramatsu, Tatsuya},
title = {Toward Robots as Embodied Knowledge Media},
journal = {IEICE Transactions on Information and Systems},
volume = {E89-D},
number = {6},
pages = {1768--1780},
year = {2006},
doi = {10.1093/ietisy/e89-d.6.1768}
}
Refereed Book Chapters
Robotics & HRI
Mohammad, Y. (2018). Natural Human-Robot Interaction. In K. L. Norman & J. Kirakowski (Eds.), The Wiley Handbook of Human Computer Interaction (Vol. 2, pp. 641–655). Wiley.
@incollection{mohammad2018naturalhri,
keywords = {bookchapter},
focus = {robotics},
author = {Mohammad, Yasser},
title = {Natural Human-Robot Interaction},
booktitle = {The Wiley Handbook of Human Computer Interaction},
editor = {Norman, Kent L. and Kirakowski, Jurek},
volume = {2},
pages = {641--655},
publisher = {Wiley},
year = {2018}
}
Nonverbal behavior during human-human close encounters is
critical to the accomplishment of natural interaction. For this
reason, humanoid robots trying to achieve natural interactions
with humans should be able to understand and synthesis nonverbal
behavior in a way that mimics the human use of it. One of the
most important situations during natural human-robot interactions
is the explanation scenario in which the human is explaining a
task to the robot using natural verbal and nonverbal behavior.
This situation occurs frequently in many HRI applications and is
critical to the success of the Robots as Knowledge Media project
suggested by the authors. In this paper the implementation of a
humanoid robot that can show human like gaze control during
explanation settings based only on reactive processing is
presented. The software of the robot is based on the EICA
architecture designed to combine autonomy with interactivity in
the lowest level of the system. The details of the implementation
and analysis of the naturalness of behavior and the effect of
noisy input is presented in this paper.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2010). Modelling Interaction Dynamics During Face-to-Face Interactions. In T. Nishida, L. C. Jain, & C. Faucher (Eds.), Modeling Machine Emotions for Realizing Intelligence —
Foundations and Applications (Vol. 1, pp. 53–87). Springer.
@incollection{mohammad2010interactiondynamics,
keywords = {bookchapter},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Modelling Interaction Dynamics During Face-to-Face Interactions},
booktitle = {Modeling Machine Emotions for Realizing Intelligence ---
Foundations and Applications},
editor = {Nishida, Toyoaki and Jain, Lakhmi C. and Faucher, Colette},
series = {Smart Innovation, Systems and Technologies},
volume = {1},
pages = {53--87},
publisher = {Springer},
year = {2010},
doi = {10.1007/978-3-642-12604-8_4}
}
During face to face interactions, the emotional state of each
participant is greatly affected by the behavior of other
participants and how much this behavior conforms with common
protocols of interaction in the society. Research in human to
human interaction in face to face situations has uncovered many
forms of synchrony in the behavior of the interacting partners.
This includes factors as body alignment, entrainment of verbal
behavior. Maintenance of these kinds of synchrony is essential to
keep the interaction natural and to regulate the affective state
of the interacting partners. In this chapter we examine the
interplay between one partner’s use of interaction protocols,
maintenance of synchrony and the emotional response of the other
partner in the two way interactions. We will first define the
notion of interaction protocol and relate it with the Reactive
Theory of Intention and Low Level Emotions. We will then show
empirically that the use of suitable interaction protocols is
essential to maintain a positive emotional response of the
interaction partner during face to face explanation situations.
The analysis in this section is based on the H 3R interaction
corpus containing sixty six human-human and human-robot
interaction sessions. This interaction corpus utilizes
physiological, behavioral and subjective data. Using this result,
it is necessary to model not only the affective state of the
interacting partners but also the interaction protocol that each
of them is using. Human-Robot interaction experiments can be of
value in analyzing the interaction protocols used by the partners
and modelling their emotional response to these protocols. We
used Human-Robot interactions in explanation and collaborative
navigation tasks as a test-bed for our analysis of interaction
protocol emergence and adaptation. The first experiment analyzes
how the requirement to maintain the interaction protocol and
synchrony restricts the design of the robot and how did we meet
these restriction in a semi-autonomous miniature robot. We focus
on how low level emotions can be used to act as a mediator
between Perception and Behavior. The second experiment explores a
computational model of the interaction protocol and evaluates it
in an explanation face to face scenario. The chapter also
provides a critical analysis of the interplay between interaction
protocols and the emotional state of interaction partners.
Interaction Dynamics, Embodied Interactive Control Architecture
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2010). Mining Causal Relationships in Multidimensional Time Series. In E. Szczerbicki & N. T. Nguyen (Eds.), Smart Information and Knowledge Management: Advances,
Challenges, and Critical Issues (Vol. 260, pp. 309–338). Springer.
@incollection{mohammad2010miningcausal,
keywords = {bookchapter, important},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Mining Causal Relationships in Multidimensional Time Series},
booktitle = {Smart Information and Knowledge Management: Advances,
Challenges, and Critical Issues},
editor = {Szczerbicki, Edward and Nguyen, Ngoc Thanh},
series = {Studies in Computational Intelligence},
volume = {260},
pages = {309--338},
publisher = {Springer},
year = {2010},
doi = {10.1007/978-3-642-04584-4_14}
}
Time series are ubiquitous in all domains of human endeavor.
They are generated, stored, and manipulated during any kind of
activity. The goal of this chapter is to introduce a novel
approach to mine multidimensional time-series data for causal
relationships. The main feature of the proposed system is
supporting discovery of causal relations based on automatically
discovered recurring patterns in the input time series. This is
achieved by integrating a variety of data mining techniques. The
main insight of the proposed system is that causal relations can
be found more easily and robustly by analyzing meaningful events
in the time series rather than by analyzing the time series
numerical values directly. The RSST (Robust Singular Spectrum
Transform) algorithm is used to find interesting points in every
time series that is further analyzed by a constrained motif
discovery algorithm (if needed) to learn basic events of the time
series. The Granger-causality test is extended and applied to the
multidimensional time-series describing the occurrences of these
basic events rather than to the raw time-series data. The
combined algorithm is evaluated using both synthetic and real
world data. The real world application is to mine records of
activities during a human-robot interaction experiment in which a
human subject is guiding a robot to navigate using free hand
gesture. The results show that the combined system can provide
causality graphs representing the underlying relations between
the human’s actions and robot behavior that cannot be recovered
using standard causal graph learning procedures. Mining Time
Series, Robust Singular Spectrum Transform, Granger-Causality,
Mining Causal Relations
Mohammad, Y., & Nishida, T. (2009). Learning Interaction Structure Using a Hierarchy of Dynamical
Systems. In B.-C. Chien & T.-P. Hong (Eds.), Opportunities and Challenges for Next-Generation Applied
Intelligence (Vol. 214, pp. 253–258). Springer.
@incollection{mohammad2009hierarchy,
keywords = {bookchapter},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Learning Interaction Structure Using a Hierarchy of Dynamical
Systems},
booktitle = {Opportunities and Challenges for Next-Generation Applied
Intelligence},
editor = {Chien, Been-Chian and Hong, Tzung-Pei},
series = {Studies in Computational Intelligence},
volume = {214},
pages = {253--258},
publisher = {Springer},
year = {2009}
}
The IAM (Interaction Adaptation Manager) algorithm was recently
proposed to learn the optimal parameters of a hierarchical
dynamical system incrementally through interacting with other
agents given that the structure of the system is known (the
number of processes in each layer and their interconnections) and
that the agent knows how to interact in all roles except the one
it is learning (e.g. an agent learning to listen should know how
to speak). This paper presents an algorithm for learning the
structure of a hierarchical dynamical system representing the
interaction protocol at various timescales and using multiple
modalities relaxing these two constraint. The proposed system was
tested in a simulation environment in which rich human-like
agents are interacting and showed accurate recognition of the
interaction structure using few training examples. The learned
structure showed acceptable performance that allowed subsequent
application of the adaptation algorithm to converge to a good
solution using as few as 15 interactions. The paper also presents
an experiment to evaluate the real-world behavior of a gaze
controller learned by the system. The results show that the
proposed algorithm outperforms another state-of-the-art gaze
controller in terms of human-likeness, apparent understanding of
the robot, and comfort of the human partner.
International Conferences
ML & Time-SeriesAutomated Negotiation
Mohammad, Y., & Chen, H. (2026). Automated Negotiation and Multimodal Time-Series Forecasting for
Efficient Procurement. Proceedings of the 25th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2026).
@inproceedings{mohammad2026forecasting,
keywords = {conference},
focus = {timeseries,negotiation},
author = {Mohammad, Yasser and Chen, Haifeng},
title = {Automated Negotiation and Multimodal Time-Series Forecasting for
Efficient Procurement},
booktitle = {Proceedings of the 25th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2026)},
address = {Paphos, Cyprus},
year = {2026}
}
Procurement is a key function in supply chain management that
involves acquiring goods and services to meet organizational
needs. Efficient procurement is crucial for minimizing costs,
ensuring timely delivery, and maintaining quality standards. This
paper explores the integration of automated negotiation and
multimodal time-series forecasting to enhance procurement
processes. Automated negotiation can streamline interactions with
suppliers, while multimodal time-series forecasting can improve
demand prediction accuracy by leveraging diverse data sources
leading to better negotiation outputs. By combining these
approaches, organizations can optimize procurement strategies,
reduce costs, and improve overall supply chain efficiency. We
present two case studies using simulations based on real-world
data for procurement that show the effectiveness of the proposed
framework.
Automated NegotiationMultiagent Systems
Aydoğan, R., Baarslag, T., Florijn, T. C. P., Fujita, K., Jonker, C. M., & Mohammad, Y. (2026). [COMP25] The Automated Negotiating Agents Competition (ANAC)
2025 Challenges and Results. CoRR, abs/2604.13914. https://arxiv.org/abs/2604.13914
@inproceedings{aydogan2026anac2025,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Aydo{\u{g}}an, Reyhan and Baarslag, Tim and Florijn, Tamara C. P. and Fujita, Katsuhide and Jonker, Catholijn M. and Mohammad, Yasser},
title = {{[COMP25]} The Automated Negotiating Agents Competition {(ANAC)}
2025 Challenges and Results},
booktitle = {CoRR},
volume = {abs/2604.13914},
year = {2026},
doi = {10.48550/arXiv.2604.13914},
eprinttype = {arXiv},
eprint = {2604.13914},
url = {https://arxiv.org/abs/2604.13914}
}
Automated Negotiation
Mohammad, Y. (2025). Adapting Bargaining Strategies to the TAU Protocol for Better
Negotiation Outcomes. ECAI 2025 — 28th European Conference on Artificial
Intelligence, 413, 4977–4984.
@inproceedings{mohammad2025adapting,
keywords = {conference},
focus = {negotiation},
author = {Mohammad, Yasser},
title = {Adapting Bargaining Strategies to the {TAU} Protocol for Better
Negotiation Outcomes},
booktitle = {ECAI 2025 --- 28th European Conference on Artificial
Intelligence},
series = {Frontiers in Artificial Intelligence and Applications},
volume = {413},
pages = {4977--4984},
publisher = {IOS Press},
address = {Bologna, Italy},
year = {2025},
doi = {10.3233/FAIA251410}
}
As artificial intelligence (AI) becomes increasingly common in
industry and business operations, researchers are focusing on how
to enable AI agents representing different stakeholders to
cooperate effectively in competitive environments. Inspired by
human negotiation, automated negotiation is being explored as a
solution. Traditional approaches like the Stacked Alternating
Offers Protocol (SAOP) for multilateral negotiations and the
Alternating Offers Protocol (AOP) for bilateral negotiations
mimic human bargaining. Recently, the Tentative Acceptance Unique
Offers Protocol (TAU) was proposed as an alternative that leads
to faster and better agreements when a simple strategy is used by
the negotiators. This paper introduces a method for adapting any
existing AOP strategy to TAU. Our results demonstrate that this
approach leads to faster and more beneficial agreements for all
parties involved, with higher overall optimality and fairness.
This improvement is achieved with a minor increase in information
revelation.
Automated Negotiation
Ando, T., Miki, N., Yanagi, N., & Mohammad, Y. (2025). Automated Negotiation for Delivery Date Adjustment in Procurement. ECAI 2025 — 28th European Conference on Artificial
Intelligence, 413, 5092–5095. Demo track.
@inproceedings{ando2025delivery,
keywords = {conference},
focus = {negotiation},
author = {Ando, Tomohito and Miki, Nozomoi and Yanagi, Norio and Mohammad, Yasser},
title = {Automated Negotiation for Delivery Date Adjustment in Procurement},
booktitle = {ECAI 2025 --- 28th European Conference on Artificial
Intelligence},
series = {Frontiers in Artificial Intelligence and Applications},
volume = {413},
pages = {5092--5095},
publisher = {IOS Press},
address = {Bologna, Italy},
year = {2025},
doi = {10.3233/FAIA251427},
note = {Demo track}
}
Negotiation is ubiquitous in business applications in general
and in procurement operations in particular. Nevertheless,
several studies have shown that the negotiation process is often
inefficient and time-consuming. In this paper, we propose a novel
automated negotiation framework for procurement focusing on
delivery date adjustment negotiations between buyers and
suppliers. These negotiations are one of the most repeated
negotiations in industrial applications.Nevertheless, they are
often complex and time-consuming, as they involve multiple
parties and require careful consideration of various internal and
external factors. The proposed method was evaluated in the field
and was shown to provide a significant reduction in the time
required to reach achievement and around 95
Multiagent SystemsAutomated Negotiation
Mohammad, Y. (2025). Tackling the Protocol Problem in Automated Negotiation. Proceedings of the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025), 2870–2874. Blue Sky Ideas track. https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p2870.pdf
@inproceedings{mohammad2025protocol,
keywords = {conference},
focus = {multiagent,negotiation},
author = {Mohammad, Yasser},
title = {Tackling the Protocol Problem in Automated Negotiation},
booktitle = {Proceedings of the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025)},
pages = {2870--2874},
publisher = {IFAAMAS/ACM},
address = {Detroit, MI, USA},
year = {2025},
doi = {10.5555/3709347.3744037},
url = {https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p2870.pdf},
note = {Blue Sky Ideas track}
}
Automated Negotiation (AN) is a research field with roots
extending back to the mid-twentieth century. There are two
dominant AN research directions pursued by the AAMAS community in
recent years: (1) designing new heuristic or ML/RL/MARL-based
strategies for the simplest bargaining mechanism called the
Alternating Offers Protocol (AOP) and its extensions and (2)
defining new mediated mechanisms that require a trusted third
party. Intelligence lies in the strategy in the first direction
and the mechanism in the latter. Either way, evaluation is almost
always conducted in terms of empirical evaluation in some chosen
set of negotiation scenarios. This paper argues for more efforts
towards tackling the problem of in automated negotiation more
rigorously by integrating ideas from mechanism-design literature.
This requires, as a first step, a common language for expressing
different negotiation protocols and strategies. We provide such a
language which can represent a wide variety of negotiation
protocols (both mediated and unmediated). We briefly outline our
early effort in using this approach to provide a novel protocol
with a provable Perfect Bayesian Equilibrium strategy that is
also empirically effective.
Automated NegotiationMultiagent Systems
Aydoğan, R., Baarslag, T., Florijn, T. C. P., Fujita, K., Jonker, C. M., & Mohammad, Y. (2025). [COMP24] The Automated Negotiating Agents Competition (ANAC)
2024 Challenges and Results. Proceedings of the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025), 3000–3002. https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p3000.pdf
@inproceedings{aydogan2025anac2024,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Aydo{\u{g}}an, Reyhan and Baarslag, Tim and Florijn, Tamara C. P. and Fujita, Katsuhide and Jonker, Catholijn M. and Mohammad, Yasser},
title = {{[COMP24]} The Automated Negotiating Agents Competition {(ANAC)}
2024 Challenges and Results},
booktitle = {Proceedings of the 24th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2025)},
pages = {3000--3002},
publisher = {IFAAMAS/ACM},
address = {Detroit, MI, USA},
year = {2025},
doi = {10.5555/3709347.3744072},
url = {https://www.ifaamas.org/Proceedings/aamas2025/pdfs/p3000.pdf}
}
Automated NegotiationMultiagent Systems
Mohammad, Y., Nakadai, S., & Greenwald, A. (2024). Automated Negotiation in Supply Chains: A Generalist Environment
for RL/MARL Research. PRIMA 2024: Principles and Practice of Multi-Agent Systems —
25th International Conference, 15395, 19–24.
@inproceedings{mohammad2024generalist,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and Nakadai, Shinji and Greenwald, Amy},
title = {Automated Negotiation in Supply Chains: A Generalist Environment
for {RL/MARL} Research},
booktitle = {PRIMA 2024: Principles and Practice of Multi-Agent Systems ---
25th International Conference},
series = {Lecture Notes in Computer Science},
volume = {15395},
pages = {19--24},
publisher = {Springer},
address = {Kyoto, Japan},
year = {2024},
doi = {10.1007/978-3-031-77367-9_2}
}
Automated NegotiationMultiagent Systems
Mohammad, Y. (2023). Generalized Bargaining Protocols. AI 2023: Advances in Artificial Intelligence — 36th
Australasian Joint Conference on Artificial Intelligence, 14472, 261–273.
@inproceedings{mohammad2023generalizedbargaining,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser},
title = {Generalized Bargaining Protocols},
booktitle = {AI 2023: Advances in Artificial Intelligence --- 36th
Australasian Joint Conference on Artificial Intelligence},
series = {Lecture Notes in Computer Science},
volume = {14472},
pages = {261--273},
publisher = {Springer},
address = {Brisbane, Australia},
year = {2023},
doi = {10.1007/978-981-99-8391-9_21}
}
Automated Negotiation (AN) is a research field with roots
extending back to the mid-twentieth century. There are two
dominant AN research directions in recent years: (1) designing
new heuristic strategies for the simplest bargaining protocol
called the Alternating Offers Protocol (AOP) and (2) defining new
mediated protocol that require a trusted third party.
Intelligence lies in the strategy in the first direction and the
protocol in the latter. This paper argues for a third way that
aims at designing unmediated AN protocols with desired
properties. We introduce a generalization of AOP to a wide class
of unmediated protocols that keep its main advantages while
providing the designer with the freedom to design protocols with
desired properties. We also introduce the first fruits of this
research direction in the form of an unmediated protocol and a
corresponding simple strategy that can be shown theoretically to
be exactly rational, optimal, and complete for bilateral
negotiations with no information about partner’s preferences.
Automated Negotiation Multiagent Systems Mechanism Design.
Automated Negotiation
Mohammad, Y. (2023). Evaluating Automated Negotiations. IEEE International Conference on Agents (ICA 2023), 77–82. Best Paper Award.
IEEE ICA 2023 Best Paper
@inproceedings{mohammad2023evaluating,
award = {IEEE ICA 2023 Best Paper},
keywords = {conference},
focus = {negotiation},
author = {Mohammad, Yasser},
title = {Evaluating Automated Negotiations},
booktitle = {IEEE International Conference on Agents (ICA 2023)},
pages = {77--82},
publisher = {IEEE},
address = {Kyoto, Japan},
year = {2023},
doi = {10.1109/ICA58824.2023.00022},
note = {Best Paper Award}
}
Automated Negotiation (AN) is a process for reaching agreement
between agents representing self-interested parties. Several
negotiation protocols and strategies have been proposed over the
years. In this paper, we argue that evaluating a negotiation
session is not straightforward and depends on several contextual
factors. The paper proposes a multifaceted evaluation criteria
for negotiation sessions that can be adjusted to the evaluation
context. The proposed evaluation criteria are then applied to
several negotiation scenarios with state-of-the-art negotiation
algorithms.
Automated NegotiationMultiagent Systems
Aydoğan, R., Baarslag, T., Fujita, K., Hoos, H. H., Jonker, C. M., Mohammad, Y., & Renting, B. M. (2022). The 13th International Automated Negotiating Agent Competition
Challenges and Results. Recent Advances in Agent-Based Negotiation: Applications And
Competition Challenges (ACAN@IJCAI 2022), 1092, 87–101.
@inproceedings{aydogan2022anac13,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Aydo{\u{g}}an, Reyhan and Baarslag, Tim and Fujita, Katsuhide and Hoos, Holger H. and Jonker, Catholijn M. and Mohammad, Yasser and Renting, Bram M.},
title = {The 13th International Automated Negotiating Agent Competition
Challenges and Results},
booktitle = {Recent Advances in Agent-Based Negotiation: Applications and
Competition Challenges (ACAN@IJCAI 2022)},
series = {Studies in Computational Intelligence},
volume = {1092},
pages = {87--101},
publisher = {Springer},
year = {2022},
doi = {10.1007/978-981-99-0561-4_5}
}
Automated Negotiation
Mohammad, Y., & Nakadai, S. (2022). Concurrent Negotiations with Global Utility Functions. Proceedings of the 21st International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2022), 1947–1949. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1947.pdf
@inproceedings{mohammad2022concurrentglobal,
keywords = {conference, important},
focus = {negotiation},
author = {Mohammad, Yasser and Nakadai, Shinji},
title = {Concurrent Negotiations with Global Utility Functions},
booktitle = {Proceedings of the 21st International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2022)},
pages = {1947--1949},
publisher = {IFAAMAS},
address = {Auckland, New Zealand},
year = {2022},
doi = {10.5555/3535850.3536162},
url = {https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1947.pdf}
}
Automated Negotiation is attracting more attention from
researchers recently as it is becoming more relevant to
industrial and business applications with increased reliance on
automated systems. Most research in this area assumes either a
single negotiation thread with a well-defined utility function
for each agent involved or a set of concurrent negotiations with
an ordering of outcomes in each local negotiation. In this paper,
we consider an agent engaged in a set of concurrent negotiations
with a utility function defined only for the in of them and no
locally defined ordering of outcomes in any negotiation
independent from what happens in the others. We argue that this
problem setting is interesting both from the academic and the
industrial points of view. The paper then presents an algorithm
that allows such agent to maximize its expected global utility by
orchestrating its behavior in all negotiation threads. The
performance of the proposed method is analyzed theoretically and
empirically using simulation.
Automated Negotiation
Sengupta, A., Nakadai, S., & Mohammad, Y. (2022). Transfer Learning Based Adaptive Automated Negotiating Agent
Framework. Proceedings of the Thirty-First International Joint Conference
on Artificial Intelligence (IJCAI 2022), 468–474. https://www.ijcai.org/proceedings/2022/0067.pdf
@inproceedings{sengupta2022transfer,
keywords = {conference},
focus = {negotiation,machinelearning},
author = {Sengupta, Ayan and Nakadai, Shinji and Mohammad, Yasser},
title = {Transfer Learning Based Adaptive Automated Negotiating Agent
Framework},
booktitle = {Proceedings of the Thirty-First International Joint Conference
on Artificial Intelligence (IJCAI 2022)},
pages = {468--474},
publisher = {ijcai.org},
address = {Vienna, Austria},
year = {2022},
doi = {10.24963/ijcai.2022/67},
url = {https://www.ijcai.org/proceedings/2022/0067.pdf}
}
ML & Time-Series
Heracleous, P., Fukayama, S., Ogata, J., & Mohammad, Y. (2022). Applying Generative Adversarial Networks and Vision Transformers in
Speech Emotion Recognition. HCI International 2022 — Late Breaking Papers (HCII 2022), 13519, 67–75.
@inproceedings{heracleous2022gan,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Fukayama, Satoru and Ogata, Jun and Mohammad, Yasser},
title = {Applying Generative Adversarial Networks and Vision Transformers in
Speech Emotion Recognition},
booktitle = {HCI International 2022 --- Late Breaking Papers (HCII 2022)},
series = {Lecture Notes in Computer Science},
volume = {13519},
pages = {67--75},
publisher = {Springer},
year = {2022},
doi = {10.1007/978-3-031-17618-0_6}
}
Robotics & HRI
Ahmed, A., Mohammad, Y. F. O., Parque, V., El-Hussieny, H., & Ahmed, S. M. (2022). End-to-End Mobile Robot Navigation Using a Residual Deep
Reinforcement Learning in Dynamic Human Environments. 18th IEEE/ASME International Conference on Mechatronic And
Embedded Systems and Applications (MESA 2022), 1–6.
@inproceedings{ahmed2022endtoend,
keywords = {conference},
focus = {robotics,machinelearning},
author = {Ahmed, Abdullah and Mohammad, Yasser F. O. and Parque, Victor and El-Hussieny, Haitham and Ahmed, Sabah M.},
title = {End-to-End Mobile Robot Navigation Using a Residual Deep
Reinforcement Learning in Dynamic Human Environments},
booktitle = {18th IEEE/ASME International Conference on Mechatronic and
Embedded Systems and Applications (MESA 2022)},
pages = {1--6},
publisher = {IEEE},
address = {Taipei, Taiwan},
year = {2022},
doi = {10.1109/MESA55290.2022.10004394}
}
Automated Negotiation
Sengupta, A., Mohammad, Y., & Nakadai, S. (2021). An Autonomous Negotiating Agent Framework with Reinforcement
Learning Based Strategies and Adaptive Strategy Switching Mechanism. Proceedings of the 20th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2021), 1163–1172. https://ifaamas.org/Proceedings/aamas2021/pdfs/p1163.pdf
@inproceedings{sengupta2021autonomous,
keywords = {conference, important},
focus = {negotiation,machinelearning},
author = {Sengupta, Ayan and Mohammad, Yasser and Nakadai, Shinji},
title = {An Autonomous Negotiating Agent Framework with Reinforcement
Learning Based Strategies and Adaptive Strategy Switching Mechanism},
booktitle = {Proceedings of the 20th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2021)},
pages = {1163--1172},
publisher = {ACM},
year = {2021},
doi = {10.5555/3463952.3464087},
url = {https://ifaamas.org/Proceedings/aamas2021/pdfs/p1163.pdf}
}
Despite abundant negotiation strategies in literature, the
complexity of automated negotiation forbids a single strategy
from being dominant against all others in different negotiation
scenarios. To overcome this, one approach is to use mixture of
experts, but at the same time one problem of this method is the
selection of experts, as this approach is limited by the
competency of the experts selected. Another problem with most
negotiation strategies is their incapability of adapting to
dynamic variation of the opponent’s behaviour within a single
negotiation session resulting in poor performance. This work
focuses on both, solving the problem of expert selection and
adapting to the opponent’s behaviour with our Autonomous
Negotiating Agent Framework. This framework allows real-time
classification of opponent’s behaviour and provides a mechanism
to select, switch or combine strategies within a single
negotiation session. Additionally, our framework has a reviewer
component which enables self-enhancement capability by deciding
to include new strategies or replace old ones with better
strategies periodically. We demonstrate an instance of our
framework by implementing maximum entropy reinforcement learning
based strategies with a deep learning based opponent classifier.
Finally, we evaluate the performance of our agent against
state-of-the-art negotiators under varied negotiation scenarios.
ML & Time-Series
Heracleous, P., Mohammad, Y., & Yoneyama, A. (2021). Speech Emotion Recognition Using Combined Multiple Pairwise
Classifiers. HCI International 2021 — Late Breaking Posters (HCII 2021), 1498, 115–118.
@inproceedings{heracleous2021pairwise,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Yoneyama, Akio},
title = {Speech Emotion Recognition Using Combined Multiple Pairwise
Classifiers},
booktitle = {HCI International 2021 --- Late Breaking Posters (HCII 2021)},
series = {Communications in Computer and Information Science},
volume = {1498},
pages = {115--118},
publisher = {Springer},
year = {2021},
doi = {10.1007/978-3-030-90176-9_16}
}
Automated NegotiationMultiagent Systems
Mohammad, Y., Nakadai, S., & Greenwald, A. (2020). NegMAS: A Platform for Automated Negotiations. PRIMA 2020: Principles and Practice of Multi-Agent Systems —
23rd International Conference, 12568, 343–351.
@inproceedings{mohammad2020negmas,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and Nakadai, Shinji and Greenwald, Amy},
title = {{NegMAS}: A Platform for Automated Negotiations},
booktitle = {PRIMA 2020: Principles and Practice of Multi-Agent Systems ---
23rd International Conference},
series = {Lecture Notes in Computer Science},
volume = {12568},
pages = {343--351},
publisher = {Springer},
address = {Nagoya, Japan},
year = {2020},
doi = {10.1007/978-3-030-69322-0_23}
}
Alongside the widespread adoption of AI technology thoughout the
business world, automated negotiation is similarly gaining more
interest within the multiagent system (MAS) research community.
This interest has prompted the development of research-oriented
automated negotiation platforms like GENIUS. This paper
introduces NegMAS, Negotiations Managed by Agent Simulations /
Negotiation MultiAgent System, which was developed to facilitate
research and development of agents that negotiate in dynamic
situations characterized by interrelated utility functions with
all negotiation related decisions managed by agents.
Automated NegotiationMultiagent Systems
Aydoğan, R., Baarslag, T., Fujita, K., Mell, J., Gratch, J., de Jonge, D., Mohammad, Y., Nakadai, S., Morinaga, S., Osawa, H., Aranha, C., & Jonker, C. M. (2020). Challenges and Main Results of the Automated Negotiating Agents
Competition (ANAC) 2019. Multi-Agent Systems and Agreement Technologies — 17th
European Conference (EUMAS 2020) and 7th International
Conference (AT 2020), 12520, 366–381.
@inproceedings{aydogan2020anac2019,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Aydo{\u{g}}an, Reyhan and Baarslag, Tim and Fujita, Katsuhide and Mell, Johnathan and Gratch, Jonathan and de Jonge, Dave and Mohammad, Yasser and Nakadai, Shinji and Morinaga, Satoshi and Osawa, Hirotaka and Aranha, Claus and Jonker, Catholijn M.},
title = {Challenges and Main Results of the Automated Negotiating Agents
Competition {(ANAC)} 2019},
booktitle = {Multi-Agent Systems and Agreement Technologies --- 17th
European Conference (EUMAS 2020) and 7th International
Conference (AT 2020)},
series = {Lecture Notes in Computer Science},
volume = {12520},
pages = {366--381},
publisher = {Springer},
address = {Thessaloniki, Greece},
year = {2020},
doi = {10.1007/978-3-030-66412-1_23}
}
ML & Time-Series
Heracleous, P., Mohammad, Y., & Yoneyama, A. (2020). Integrating Language and Emotion Features for Multilingual Speech
Emotion Recognition. Human-Computer Interaction. Multimodal and Natural Interaction
(HCII 2020), 12182, 187–196.
@inproceedings{heracleous2020multilingual,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Yoneyama, Akio},
title = {Integrating Language and Emotion Features for Multilingual Speech
Emotion Recognition},
booktitle = {Human-Computer Interaction. Multimodal and Natural Interaction
(HCII 2020)},
series = {Lecture Notes in Computer Science},
volume = {12182},
pages = {187--196},
publisher = {Springer},
address = {Copenhagen, Denmark},
year = {2020},
doi = {10.1007/978-3-030-49062-1_12}
}
ML & Time-Series
Heracleous, P., Takai, K., Wang, Y., Yasuda, K., Yoneyama, A., & Mohammad, Y. (2020). An Empirical Study on Feature Extraction in DNN-Based Speech
Emotion Recognition. HCI International 2020 — Late Breaking Posters (HCII 2020), 1293, 315–319.
@inproceedings{heracleous2020empirical,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Takai, Kohichi and Wang, Yanan and Yasuda, Keiji and Yoneyama, Akio and Mohammad, Yasser},
title = {An Empirical Study on Feature Extraction in {DNN}-Based Speech
Emotion Recognition},
booktitle = {HCI International 2020 --- Late Breaking Posters (HCII 2020)},
series = {Communications in Computer and Information Science},
volume = {1293},
pages = {315--319},
publisher = {Springer},
address = {Copenhagen, Denmark},
year = {2020},
doi = {10.1007/978-3-030-60700-5_40}
}
Multiagent SystemsAutomated Negotiation
Mohammad, Y. (2020). Optimal Deterministic Time-Based Policy in Automated Negotiation. PRIMA 2020: Principles and Practice of Multi-Agent Systems —
23rd International Conference, 12568, 68–83. Best Paper Award runner-up.
PRIMA 2020 Best Paper Runner-up
@inproceedings{mohammad2020optimaldeterministic,
award = {PRIMA 2020 Best Paper Runner-up},
keywords = {conference},
focus = {multiagent,negotiation},
author = {Mohammad, Yasser},
title = {Optimal Deterministic Time-Based Policy in Automated Negotiation},
booktitle = {PRIMA 2020: Principles and Practice of Multi-Agent Systems ---
23rd International Conference},
series = {Lecture Notes in Computer Science},
volume = {12568},
pages = {68--83},
publisher = {Springer},
address = {Nagoya, Japan},
year = {2020},
doi = {10.1007/978-3-030-69322-0_5},
note = {Best Paper Award runner-up}
}
Automated negotiation is gaining more attention as a possible
mechanism for organizing self-interested intelligent agents in a
distributed environment. The problem of designing effective
negotiation strategies in such environments was studied
extensively by researchers from economics, computer science,
multiagent systems, and AI. This paper focuses on the problem of
finding effective deterministic time-based offering strategies
given an opponent acceptance model. This problem was studied
earlier and optimal solutions are known for the simplest case of
a static stationary acceptance model. This paper proposes an
efficient approach for calculating the effect of different
manipulations of the offering policy on expected utility and uses
that to provide a faster implementation of the optimal algorithm
for static stationary acceptance models and provide an
approximate extension to more realistic acceptance models.
ML & Time-Series
Heracleous, P., Mohammad, Y., Yasuda, K., & Yoneyama, A. (2019). Speech Emotion Recognition Using Spontaneous Children’s Corpus. Computational Linguistics and Intelligent Text Processing —
20th International Conference (CICLing 2019), 13452, 321–333.
@inproceedings{heracleous2019children,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Yasuda, Keiji and Yoneyama, Akio},
title = {Speech Emotion Recognition Using Spontaneous Children's Corpus},
booktitle = {Computational Linguistics and Intelligent Text Processing ---
20th International Conference (CICLing 2019)},
series = {Lecture Notes in Computer Science},
volume = {13452},
pages = {321--333},
publisher = {Springer},
address = {La Rochelle, France},
year = {2019},
doi = {10.1007/978-3-031-24340-0_24}
}
Automatic recognition of human emotions is a relatively new
field and is attracting significant attention in research and
development areas because of the major contribution it could make
to real applications. Previously, several studies reported speech
emotion recognition using acted emotional corpus. For real world
applications, however, spontaneous corpora should be used in
recognizing human emotions from speech. This study focuses on
speech emotion recognition using the FAU Aibo spontaneous
children’s corpus. A method based on the integration of
feed-forward deep neural networks (DNN) and the i-vector paradigm
is proposed, and another method based on deep convolutional
neural networks (DCNN) for feature extraction and extremely
randomized trees as classifier is presented. For the
classification of five emotions using balanced data, the proposed
methods showed unweighted average recalls (UAR) of 61.1
ML & Time-Series
Heracleous, P., Mohammad, Y., & Yoneyama, A. (2019). Deep Convolutional Neural Networks for Feature Extraction in Speech
Emotion Recognition. Human-Computer Interaction. Recognition and Interaction
Technologies (HCII 2019), 11567, 117–132.
@inproceedings{heracleous2019deepcnn,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Yoneyama, Akio},
title = {Deep Convolutional Neural Networks for Feature Extraction in Speech
Emotion Recognition},
booktitle = {Human-Computer Interaction. Recognition and Interaction
Technologies (HCII 2019)},
series = {Lecture Notes in Computer Science},
volume = {11567},
pages = {117--132},
publisher = {Springer},
address = {Orlando, FL, USA},
year = {2019},
doi = {10.1007/978-3-030-22643-5_9}
}
ML & Time-Series
Rayan, Y., Mohammad, Y. F. O., & Ali, S. A. (2019). Multidimensional Permutation Entropy for Constrained Motif
Discovery. Intelligent Information and Database Systems — 11th Asian
Conference (ACIIDS 2019), 11431, 231–243.
@inproceedings{rayan2019multidimensional,
keywords = {conference},
focus = {timeseries},
author = {Rayan, Yomna and Mohammad, Yasser F. O. and Ali, Samia A.},
title = {Multidimensional Permutation Entropy for Constrained Motif
Discovery},
booktitle = {Intelligent Information and Database Systems --- 11th Asian
Conference (ACIIDS 2019)},
series = {Lecture Notes in Computer Science},
volume = {11431},
pages = {231--243},
publisher = {Springer},
address = {Yogyakarta, Indonesia},
year = {2019},
doi = {10.1007/978-3-030-14799-0_20}
}
Automated NegotiationMultiagent Systems
Mohammad, Y., Areyan Viqueira, E., Ayerza, N. A., Greenwald, A., Nakadai, S., & Morinaga, S. (2019). Supply Chain Management World — A Benchmark Environment for
Situated Negotiations. PRIMA 2019: Principles and Practice of Multi-Agent Systems —
22nd International Conference, 11873, 153–169.
@inproceedings{mohammad2019scmworld,
keywords = {conference},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and {Areyan Viqueira}, Enrique and Ayerza, Nahum Alvarez and Greenwald, Amy and Nakadai, Shinji and Morinaga, Satoshi},
title = {Supply Chain Management World --- A Benchmark Environment for
Situated Negotiations},
booktitle = {PRIMA 2019: Principles and Practice of Multi-Agent Systems ---
22nd International Conference},
series = {Lecture Notes in Computer Science},
volume = {11873},
pages = {153--169},
publisher = {Springer},
address = {Turin, Italy},
year = {2019},
doi = {10.1007/978-3-030-33792-6_10}
}
Automated NegotiationMultiagent Systems
Areyan Viqueira, E., Cousins, C., Mohammad, Y., & Greenwald, A. (2019). Empirical Mechanism Design: Designing Mechanisms from Data. Proceedings of the Thirty-Fifth Conference on Uncertainty In
Artificial Intelligence (UAI 2019), 115, 1094–1104. https://proceedings.mlr.press/v115/viqueira20a.html
@inproceedings{viqueira2019empirical,
keywords = {conference},
focus = {negotiation,multiagent},
author = {{Areyan Viqueira}, Enrique and Cousins, Cyrus and Mohammad, Yasser and Greenwald, Amy},
title = {Empirical Mechanism Design: Designing Mechanisms from Data},
booktitle = {Proceedings of the Thirty-Fifth Conference on Uncertainty in
Artificial Intelligence (UAI 2019)},
series = {Proceedings of Machine Learning Research},
volume = {115},
pages = {1094--1104},
publisher = {AUAI Press},
address = {Tel Aviv, Israel},
year = {2019},
url = {https://proceedings.mlr.press/v115/viqueira20a.html}
}
Automated Negotiation
Mohammad, Y. F. O., & Nakadai, S. (2019). Optimal Value of Information Based Elicitation During Negotiation. Proceedings of the 18th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2019), 242–250. https://www.ifaamas.org/Proceedings/aamas2019/pdfs/p242.pdf
@inproceedings{mohammad2019voi,
keywords = {conference},
focus = {negotiation},
author = {Mohammad, Yasser F. O. and Nakadai, Shinji},
title = {Optimal Value of Information Based Elicitation During Negotiation},
booktitle = {Proceedings of the 18th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2019)},
pages = {242--250},
publisher = {IFAAMAS},
address = {Montreal, QC, Canada},
year = {2019},
url = {https://www.ifaamas.org/Proceedings/aamas2019/pdfs/p242.pdf}
}
Autonomous agents engaging in automatic negotiations on behalf
of humans or institutions are usually assumed to have full
knowledge of the utility function for the actors they represent.
In many cases, these utility functions are difficult to know
apriori for every possible outcome of the negotiation. Moreover,
it may not be necessary for the agent to know the utility of
outcomes that are never offered or considered during the
negotiation. State-of-the-art approaches to utility elicitation
during negotiation assume that the agent can ask from a
predefined countable set to reduce its uncertainty about the
utility function. This paper extends that body of work by lifting
the countability assumption providing an optimal algorithm for
selecting the best outcome and utility level about which to ask
the actor. The paper reports the results of comparing the
proposed algorithm with state-of-the-art algorithms using both
synthetic and realistic negotiation scenarios. These evaluations
support the applicability of the proposed approach.
Automated Negotiation
Mohammad, Y. F. O., & Nakadai, S. (2018). Utility Elicitation During Negotiation with Practical Elicitation
Strategies. IEEE International Conference on Systems, Man, and Cybernetics
(SMC 2018), 3100–3107.
@inproceedings{mohammad2018elicitation,
keywords = {conference},
focus = {negotiation},
author = {Mohammad, Yasser F. O. and Nakadai, Shinji},
title = {Utility Elicitation During Negotiation with Practical Elicitation
Strategies},
booktitle = {IEEE International Conference on Systems, Man, and Cybernetics
(SMC 2018)},
pages = {3100--3107},
publisher = {IEEE},
address = {Miyazaki, Japan},
year = {2018},
doi = {10.1109/SMC.2018.00525}
}
Automatic negotiation is gaining more interest recently thanks
to the wider deployment of intelligent systems and the need for
them to cooperate/compete on behalf of their users. A central
assumption of most autonomous negotiation agents is that the
utility function of the user is perfectly known to the agent.
That is an often unmet assumption in real situations. Utility
elicitation is the process of learning about the utility function
of the user incrementally and has a long history in decision
support research. Recently, some utility elicitation systems
capable of incrementally eliciting the utility function of the
user during the negotiation were presented. This work expands
this body of research by optimizing the elicitation algorithm to
realistic elicitation strategies. The proposed method extends the
optimal elicitation algorithm to the – practical – case where
queries to the user only reduce the uncertainty in the utility
function without removing it completely. Extensive evaluation
shows that the proposed extension outperforms two
state-of-the-art elicitation algorithms and several baseline
alternatives.
ML & Time-Series
Heracleous, P., Mohammad, Y., Takai, K., Yasuda, K., & Yoneyama, A. (2018). Spoken Language Identification Based on I-Vectors and Conditional
Random Fields. 14th International Wireless Communications & Mobile Computing
Conference (IWCMC 2018), 1443–1447.
@inproceedings{heracleous2018crf,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Takai, Kohichi and Yasuda, Keiji and Yoneyama, Akio},
title = {Spoken Language Identification Based on I-Vectors and Conditional
Random Fields},
booktitle = {14th International Wireless Communications \& Mobile Computing
Conference (IWCMC 2018)},
pages = {1443--1447},
publisher = {IEEE},
address = {Limassol, Cyprus},
year = {2018},
doi = {10.1109/IWCMC.2018.8450327}
}
ML & Time-Series
Heracleous, P., Mohammad, Y., Takai, K., Yasuda, K., & Yoneyama, A. (2018). I-Vectors and Deep Convolutional Neural Networks for Language
Identification in Clean and Reverberant Environments. Computational Linguistics and Intelligent Text Processing —
19th International Conference (CICLing 2018), 13396, 30–40.
@inproceedings{heracleous2018ivectors,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Takai, Kohichi and Yasuda, Keiji and Yoneyama, Akio},
title = {I-Vectors and Deep Convolutional Neural Networks for Language
Identification in Clean and Reverberant Environments},
booktitle = {Computational Linguistics and Intelligent Text Processing ---
19th International Conference (CICLing 2018)},
series = {Lecture Notes in Computer Science},
volume = {13396},
pages = {30--40},
publisher = {Springer},
address = {Hanoi, Vietnam},
year = {2018},
doi = {10.1007/978-3-031-23793-5_3}
}
In the current study, a method for automatic language
identification based on deep convolutional neural networks (DCNN)
and the i-vector paradigm is proposed. Convolutional neural
networks (CNN) have been successfully applied to image
classification, speech emotion recognition, and facial expression
recognition. In the current study, a variant of typical CNN is
being applied and experimentally investigated in spoken language
identification. When the proposed method was evaluated on the
NIST 2015 i-vector Machine Learning Challenge task for the
recognition of 50 in-set languages, a 3.9
ML & Time-Series
Heracleous, P., Mohammad, Y., Takai, K., Yasuda, K., Yoneyama, A., & Sugaya, F. (2018). A Study on Far-Field Emotion Recognition Based on Deep
Convolutional Neural Networks. Computational Linguistics and Intelligent Text Processing —
19th International Conference (CICLing 2018), 13397, 181–193.
@inproceedings{heracleous2018farfield,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Takai, Kohichi and Yasuda, Keiji and Yoneyama, Akio and Sugaya, Fumiaki},
title = {A Study on Far-Field Emotion Recognition Based on Deep
Convolutional Neural Networks},
booktitle = {Computational Linguistics and Intelligent Text Processing ---
19th International Conference (CICLing 2018)},
series = {Lecture Notes in Computer Science},
volume = {13397},
pages = {181--193},
publisher = {Springer},
address = {Hanoi, Vietnam},
year = {2018},
doi = {10.1007/978-3-031-23804-8_15}
}
Automatic recognition of human emotions is a relatively new
field, and is attracting significant attention in research and
development areas because of the major contribution it could make
to real applications. The current study focuses on far-field
speech emotion recognition using the state-of-the-art spontaneous
IEMOCAP emotional data. For classification, a method based on
deep convolutional neural networks (DCNN) and extremely
randomized trees is proposed. The method is also compared to
support vector machines (SVM) and probabilistic linear
discriminant analysis (PLDA) classifiers in the i-vector
paradigm. When reverberant speech was classified using the
proposed method, the classification rates were comparable to
those obtained when using clean data. In the case of PLDA and SVM
classifiers, the classification rates were significantly
decreased. To further improve the performance of far-field speech
emotion recognition, a method based on multi-style training is
proposed, which results in significant improvements in the
classification rates.
ML & Time-Series
Heracleous, P., Takai, K., Yasuda, K., Mohammad, Y., & Yoneyama, A. (2018). Comparative Study on Spoken Language Identification Based on Deep
Learning. 26th European Signal Processing Conference (EUSIPCO 2018), 2265–2269.
@inproceedings{heracleous2018comparative,
keywords = {conference},
focus = {timeseries},
author = {Heracleous, Panikos and Takai, Kohichi and Yasuda, Keiji and Mohammad, Yasser and Yoneyama, Akio},
title = {Comparative Study on Spoken Language Identification Based on Deep
Learning},
booktitle = {26th European Signal Processing Conference (EUSIPCO 2018)},
pages = {2265--2269},
publisher = {IEEE},
address = {Rome, Italy},
year = {2018},
doi = {10.23919/EUSIPCO.2018.8553347}
}
Automated Negotiation
Mohammad, Y. F. O., & Nakadai, S. (2018). FastVOI: Efficient Utility Elicitation During Negotiations. PRIMA 2018: Principles and Practice of Multi-Agent Systems —
21st International Conference, 11224, 560–567.
@inproceedings{mohammad2018fastvoi,
keywords = {conference},
focus = {negotiation},
author = {Mohammad, Yasser F. O. and Nakadai, Shinji},
title = {{FastVOI}: Efficient Utility Elicitation During Negotiations},
booktitle = {PRIMA 2018: Principles and Practice of Multi-Agent Systems ---
21st International Conference},
series = {Lecture Notes in Computer Science},
volume = {11224},
pages = {560--567},
publisher = {Springer},
address = {Tokyo, Japan},
year = {2018},
doi = {10.1007/978-3-030-03098-8_42}
}
Autonomous Negotiation is a promising technology that allows
individuals and institutions to reduce the burden and cost of
negotiating win-win agreements. A common challenge in practical
applications is the inability or high cost of finding the utility
value for each possible outcome of the negotiation before it even
starts. Earlier work on utility elicitation during negotiations
tried to avoid the need of full revelation of the utility
function to the agent by interleaving elicitation and negotiation
actions. This paper proposes an efficient elicitation algorithm
that allows the agent to achieve similar utility at orders of
magnitude higher speed compared with the state-of-the-art
algorithm. Autonomous Negotiation Utility Elicitation.
ML & Time-Series
Mohammad, Y. F. O., Matsumoto, K., & Hoashi, K. (2018). Deep Feature Learning and Selection for Activity Recognition. Proceedings of the 33rd Annual ACM Symposium on Applied
Computing (SAC 2018), 930–939.
@inproceedings{mohammad2018deepfeature,
keywords = {conference},
focus = {timeseries,machinelearning},
author = {Mohammad, Yasser F. O. and Matsumoto, Kazunori and Hoashi, Keiichiro},
title = {Deep Feature Learning and Selection for Activity Recognition},
booktitle = {Proceedings of the 33rd Annual ACM Symposium on Applied
Computing (SAC 2018)},
pages = {930--939},
publisher = {ACM},
address = {Pau, France},
year = {2018},
doi = {10.1145/3167132.3167234}
}
Human physical activity recognition from sensor data is a
growing area of research due to the widespread adoption of
sensor-rich wearable and smart devices. The growing interest
resulted in several formulations with multiple proposals for each
of them. This paper is interested in activity recognition from
short sequences of sensor readings. Traditionally, solutions to
this problem have relied on handcrafted features and feature
selection from large predefined feature sets. More recently, deep
methods have been employed to provide an end-to-end
classification system for activity recognition with higher
accuracy at the expense of much slower performance. This paper
proposes a middle ground in which a deep neural architecture is
employed for feature learning followed by traditional feature
selection and classification. This approach is shown to
outperform state-of-the-art systems on six out of seven
experiments using publicly available datasets.
ML & Time-Series
Mohammad, Y. F. O., Matsumoto, K., & Hoashi, K. (2017). A Dataset for Activity Recognition in an Unmodified Kitchen Using
Smart-Watch Accelerometers. Proceedings of the 16th International Conference on Mobile And
Ubiquitous Multimedia (MUM 2017), 63–68.
@inproceedings{mohammad2017kitchen,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Matsumoto, Kazunori and Hoashi, Keiichiro},
title = {A Dataset for Activity Recognition in an Unmodified Kitchen Using
Smart-Watch Accelerometers},
booktitle = {Proceedings of the 16th International Conference on Mobile and
Ubiquitous Multimedia (MUM 2017)},
pages = {63--68},
publisher = {ACM},
address = {Stuttgart, Germany},
year = {2017},
doi = {10.1145/3152832.3152844}
}
Robotics & HRI
Hussein, M., Mohammad, Y., Ali, S. A., & Nishida, T. (2017). COLD: A ROS Package for Continuous Learning from Demonstration:
Teaching a Robot to Write. IEEE International Conference on Mechatronics and Automation
(ICMA 2017), 651–657.
@inproceedings{hussein2017cold,
keywords = {conference},
focus = {robotics},
author = {Hussein, Mostafa and Mohammad, Yasser and Ali, Samia A. and Nishida, Toyoaki},
title = {{COLD}: A {ROS} Package for Continuous Learning from Demonstration:
Teaching a Robot to Write},
booktitle = {IEEE International Conference on Mechatronics and Automation
(ICMA 2017)},
pages = {651--657},
publisher = {IEEE},
address = {Takamatsu, Japan},
year = {2017}
}
Learning From Demonstration (LfD) is an important area of
research in robotics because it provides the means for skill
transfer from humans to robots with no reliance on any specific
technical skills of the human teacher. There are many approaches
to LfD. Most available methods assume the existence of clear
boundaries between different demonstrations. Less research
focuses on techniques that can be used by the robot to learn from
continuous streams of data that provide not only planned but also
unplanned demonstrations. This paper reports an integrated
framework to tackle this problem using the Robot Operating System
(ROS) that is modular, scalable, easily adaptable, and
robot-independent. We also report a study applying this method to
teach robots how to write in both English and Arabic by just
watching people writing short reports. Not only can the learner
write words that exist in the training data but novel words based
on the basic strokes learned from the demonstrations. The system
was applied to teaching both a physical NAO robot and a Parallel
robot simulator and evaluations demonstrate the effectiveness of
the proposed framework in this task.
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2016). MC\^2: An Integrated Toolbox for Change, Causality and Motif
Discovery. Trends in Applied Knowledge-Based Systems and Data Science —
29th International Conference on Industrial, Engineering And
Other Applications of Applied Intelligent Systems (IEA/AIE 2016)
, 9799, 128–141.
@inproceedings{mohammad2016mc2,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {{MC\^{}2}: An Integrated Toolbox for Change, Causality and Motif
Discovery},
booktitle = {Trends in Applied Knowledge-Based Systems and Data Science ---
29th International Conference on Industrial, Engineering and
Other Applications of Applied Intelligent Systems (IEA/AIE 2016)
},
series = {Lecture Notes in Computer Science},
volume = {9799},
pages = {128--141},
publisher = {Springer},
address = {Morioka, Japan},
year = {2016},
doi = {10.1007/978-3-319-42007-3_12}
}
Time series are being generated continuously from all kinds of
human endeavors. The ubiquity of time-series data generates a
need for data mining and pattern discovery algorithms targeting
this data format which is becoming of ever increasing importance.
Three basic problems in mining time-series data are change point
discovery, causality discovery and motif discovery. This paper
presents an integrated toolbox that can be used to perform any of
these tasks on multidimensional real-valued time-series using
state of the art algorithms. The proposed toolbox provides
practitioners in time-series analysis and data mining with
several tools useful for data generation, preprocessing, modeling
evaluation and mining of long sequences. As a use–case of the
toolbox, we provide a comparison between three variants of the
GEMODA algorithm applied to real–valued time-series data and a
state-of-the-art stochastic MD algorithm. The paper also reports
real world applications that uses the toolbox in HRI,
physiological signal processing, and human behavior modeling and
understanding. Change Point Discovery, Motif Discovery, Causality
Analysis
Robotics & HRI
Hussein, M., Mohammad, Y., & Ali, S. A. (2015). Learning from Demonstration Using Variational Bayesian Inference. 28th International Conference on Industrial, Engineering And
Other Applications of Applied Intelligent Systems (IEA/AIE 2015)
, 371–381.
@inproceedings{hussein2015variational,
keywords = {conference},
focus = {robotics},
author = {Hussein, Mostafa and Mohammad, Yasser and Ali, Samia A.},
title = {Learning from Demonstration Using Variational Bayesian Inference},
booktitle = {28th International Conference on Industrial, Engineering and
Other Applications of Applied Intelligent Systems (IEA/AIE 2015)
},
pages = {371--381},
publisher = {Springer},
address = {Seoul, South Korea},
year = {2015}
}
Nishida, T., Abe, M., Ookaki, T., Lala, D., Thovutikul, S., Song, H., Mohammad, Y. F. O., Nitschke, C., Ohmoto, Y., Nakazawa, A., Shochi, T., Rouas, J.-L., Bugeau, A., Lotte, F., Ming, Z., Letournel, G., Guerry, M., & Fourer, D. (2015). Synthetic Evidential Study as Augmented Collective Thought Process
— Preliminary Report. Intelligent Information and Database Systems — 7th Asian
Conference (ACIIDS 2015), 9011, 13–22.
@inproceedings{nishida2015synthetic,
keywords = {conference},
author = {Nishida, Toyoaki and Abe, Masakazu and Ookaki, Takashi and Lala, Divesh and Thovutikul, Sutasinee and Song, Hengjie and Mohammad, Yasser F. O. and Nitschke, Christian and Ohmoto, Yoshimasa and Nakazawa, Atsushi and Shochi, Takaaki and Rouas, Jean-Luc and Bugeau, Aur{\'e}lie and Lotte, Fabien and Ming, Zuheng and Letournel, Geoffrey and Guerry, Marine and Fourer, Dominique},
title = {Synthetic Evidential Study as Augmented Collective Thought Process
--- Preliminary Report},
booktitle = {Intelligent Information and Database Systems --- 7th Asian
Conference (ACIIDS 2015)},
series = {Lecture Notes in Computer Science},
volume = {9011},
pages = {13--22},
publisher = {Springer},
address = {Bali, Indonesia},
year = {2015},
doi = {10.1007/978-3-319-15702-3_2}
}
Robotics & HRI
Mohammad, Y., & Nishida, T. (2015). Simple Incremental GMM Modeling Using Multidimensional Piecewise
Linear Segmentation for Learning from Demonstration. 3rd International Conference on Industrial Application
Engineering (ICIAE 2015). Best Presentation Award.
ICIAE 2015 Best Presentation
@inproceedings{mohammad2015gmm,
award = {ICIAE 2015 Best Presentation},
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Simple Incremental {GMM} Modeling Using Multidimensional Piecewise
Linear Segmentation for Learning from Demonstration},
booktitle = {3rd International Conference on Industrial Application
Engineering (ICIAE 2015)},
address = {Kitakyushu, Japan},
year = {2015},
note = {Best Presentation Award}
}
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2014). Exact Discovery of Length-Range Motifs. Intelligent Information and Database Systems — 6th Asian
Conference (ACIIDS 2014), 8398, 23–32.
@inproceedings{mohammad2014lengthrange,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Exact Discovery of Length-Range Motifs},
booktitle = {Intelligent Information and Database Systems --- 6th Asian
Conference (ACIIDS 2014)},
series = {Lecture Notes in Computer Science},
volume = {8398},
pages = {23--32},
publisher = {Springer},
address = {Bangkok, Thailand},
year = {2014},
doi = {10.1007/978-3-319-05458-2_3}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2014). Why Should We Imitate Robots? Proceedings of the 13th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2014), 1499–1500. Extended abstract.
@inproceedings{mohammad2014whyimitate,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Why Should We Imitate Robots?},
booktitle = {Proceedings of the 13th International Conference on Autonomous
Agents and Multiagent Systems (AAMAS 2014)},
pages = {1499--1500},
publisher = {IFAAMAS/ACM},
address = {Paris, France},
year = {2014},
note = {Extended abstract}
}
Learning through imitation is a promising technology for robots
that inhabit human’s physical and social spaces. Previous
research in HRI have shown that human’s subjective evaluation of
robot’s abilities affect the way people interact with robots.
Given that one of the major challenges in learning from
demonstration (imitation) in robotics is the limited number of
training examples that the demonstrator is usually willing to
provide, it would be beneficial to design the interaction context
in such a way to increase human’s subjective evaluation of the
robot’s imitative skills. We propose back imitation as a way to
achieve that goal. Two experiments were conducted – involving 36
subjects and 124 sessions– to evaluate the effect of back
imitation (and a variant called mutual imitation) on human’s
subjective evaluation of the robot along several dimensions
including imitation skill, motion human likeness, interaction
quality, humanness and likability. The paper reports the results
of these experiments and discusses their implications for the
design of imitation interactions.
Robotics & HRI
Lala, D., Nishida, T., & Mohammad, Y. F. O. (2014). A Joint Activity Theory Analysis of Body Interactions in
Multiplayer Virtual Basketball. Proceedings of the 28th International BCS Human Computer
Interaction Conference (BCS-HCI 2014).
@inproceedings{lala2014jointactivity,
keywords = {conference},
focus = {robotics},
author = {Lala, Divesh and Nishida, Toyoaki and Mohammad, Yasser F. O.},
title = {A Joint Activity Theory Analysis of Body Interactions in
Multiplayer Virtual Basketball},
booktitle = {Proceedings of the 28th International BCS Human Computer
Interaction Conference (BCS-HCI 2014)},
series = {Workshops in Computing},
publisher = {British Computer Society},
address = {Southport, UK},
year = {2014}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2014). Human-Like Motion of a Humanoid in a Shadowing Task. International Conference on Collaboration Technologies And
Systems (CTS 2014), Workshop on Collaborative Robots and Human
Robot Interaction (CR-HRI), 123–130.
@inproceedings{mohammad2014shadowing,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Human-Like Motion of a Humanoid in a Shadowing Task},
booktitle = {International Conference on Collaboration Technologies and
Systems (CTS 2014), Workshop on Collaborative Robots and Human
Robot Interaction (CR-HRI)},
pages = {123--130},
publisher = {IEEE},
address = {Minneapolis, MN, USA},
year = {2014},
doi = {10.1109/CTS.2014.6867553}
}
Humanoid robots have –by definition– some level of
human-likeness in body form. According to previous research in
HRI, this leads to a higher expectation of human-like behavior.
Nevertheless, human-likeness is not an easy notion to define for
motion even in a task as straight forward as real-time motion
copying (the shadowing task) as this paper will try to argue. The
main hypothesis of this paper is that subjective evaluation of
robot’s motion’s human-likeness depends not only on the objective
similarity between robot’s motion and human motion but also on
the interaction context (e.g. whether or not the human have
previously engaged in mutual or back imitation with the robot).
Moreover, the paper proposes two features of motion similarity
that affect subjective evaluation of human-likeness and accuracy
in the shadowing task and shows that human-likeness is a
different attribution dimension form both accuracy and humanness
(measured using human-nature traits). The paper reports a
controlled user study involving 36 participants and 108 HRI
sessions to support these claims.
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2014). Scale Invariant Multi-Length Motif Discovery. Modern Advances in Applied Intelligence — 27th International
Conference on Industrial, Engineering and Other Applications Of
Applied Intelligent Systems (IEA/AIE 2014), 8482, 417–426.
@inproceedings{mohammad2014scaleinvariant,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Scale Invariant Multi-Length Motif Discovery},
booktitle = {Modern Advances in Applied Intelligence --- 27th International
Conference on Industrial, Engineering and Other Applications of
Applied Intelligent Systems (IEA/AIE 2014)},
series = {Lecture Notes in Computer Science},
volume = {8482},
pages = {417--426},
publisher = {Springer},
address = {Kaohsiung, Taiwan},
year = {2014},
doi = {10.1007/978-3-319-07467-2_44}
}
Discovering approximately recurrent motifs (ARMs) in timeseries
is an active area of research in data mining. Exact motif
discovery was later defined as the problem of efficiently finding
the most similar pairs of timeseries subsequences and can be used
as a basis for discovering ARMs. The most efficient algorithm for
solving this problem is the MK algorithm which was designed to
find a single pair of timeseries subsequences with maximum
similarity at a known length. Available exact solutions to the
problem of finding top K similar subsequence pairs at multiple
lengths (which can be the basis of ARM discovery) are not scale
invariant. This paper proposes a new algorithm for solving this
problem efficiently using scale invariant distance functions and
applies it to both real and synthetic dataset.
Robotics & HRI
Tatsumi, S., Mohammad, Y. F. O., Ohmoto, Y., & Nishida, T. (2014). Detection of Hidden Laughter for Human-Agent Interaction. 18th International Conference on Knowledge-Based And
Intelligent Information and Engineering Systems (KES 2014), 35, 1053–1062.
@inproceedings{tatsumi2014laughter,
keywords = {conference},
focus = {robotics},
author = {Tatsumi, Shiho and Mohammad, Yasser F. O. and Ohmoto, Yoshimasa and Nishida, Toyoaki},
title = {Detection of Hidden Laughter for Human-Agent Interaction},
booktitle = {18th International Conference on Knowledge-Based and
Intelligent Information and Engineering Systems (KES 2014)},
series = {Procedia Computer Science},
volume = {35},
pages = {1053--1062},
publisher = {Elsevier},
address = {Gdynia, Poland},
year = {2014},
doi = {10.1016/j.procs.2014.08.192}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2014). Effect of Back and Mutual Imitation on Human’s Perception of a
Humanoid’s Imitative Skill. The 23rd IEEE International Symposium on Robot and Human
Interactive Communication (RO-MAN 2014), 788–795.
@inproceedings{mohammad2014backmutual,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Effect of Back and Mutual Imitation on Human's Perception of a
Humanoid's Imitative Skill},
booktitle = {The 23rd IEEE International Symposium on Robot and Human
Interactive Communication (RO-MAN 2014)},
pages = {788--795},
publisher = {IEEE},
address = {Edinburgh, UK},
year = {2014},
doi = {10.1109/ROMAN.2014.6926349}
}
A promising technology for teaching robots new skills is
imitation learning. For robots to learn through from
demonstrations, humans should be motivated to teach them by
providing the required demonstrations and feedback for robot’s
trials at imitation. HRI research has shown that human’s
perception of robot’s skills affect robot’s acceptability and
human willingness to interact with it. Even though there is
currently large literature in imitation learning and learning
from demonstrations (LfD), little attention was given to studying
methods to improve humans’ perception of robot’s imitative skill
(other than objectively improving these skills). In this paper,
we study the effect of back and simple mutual imitation on
human’s perception of robot’s imitative skill and argue that both
of these simple manipulations of the interaction improve human’s
subjective evaluation of the robot’s imitative skill in terms of
accuracy and overall performance while having no effect on
subjective evaluation of naturalness and speed.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2014). Robust Learning from Demonstrations Using Multidimensional SAX. 14th International Conference on Control, Automation And
Systems (ICCAS 2014), 64–71. Outstanding Paper Award.
ICCAS 2014 Outstanding Paper
@inproceedings{mohammad2014robustlfd,
award = {ICCAS 2014 Outstanding Paper},
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Robust Learning from Demonstrations Using Multidimensional {SAX}},
booktitle = {14th International Conference on Control, Automation and
Systems (ICCAS 2014)},
pages = {64--71},
publisher = {IEEE},
address = {Seoul, South Korea},
year = {2014},
note = {Outstanding Paper Award}
}
Learning from demonstrations (LfD) is gaining more popularity in
robotics due to its promise of providing a human-friendly
technique for teaching robots new skills by robotics-naive users.
The two main approaches to LfD are dynamic motor primitives (DMP)
which models demonstrated motions as dynamical systems with the
advantage flexibility in changing the motion’s starting position,
goal or speed and Gaussian Mixture Modelling/ Gaussian Mixture
Regression (GMM/GMR) which represents demonstrated motions as
mixtures of Gaussians with the advantage of keeping track of the
correlations between different dimensions of learned motions and
automatic extraction of motion variability along these
dimensions. This paper introduces a third approach that relies on
symbolization of demonstrated motions by extending the Symbolic
Aggregate approXimation (SAX) to handle multiple dimensions of
data. The proposed approach is shown through several synthetic
and real-data evaluations to be more resistant to bursts of noise
that usually appear in motion capture data used to represent the
demonstrations. The paper also discusses possible ways to combine
SAX based LfD with DMP and GMM/GMR in order to preserve the
advantages of these two approaches while providing superior burst
noise resistance.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2013). Tackling the Correspondence Problem — Closed-Form Solution for
Gesture Imitation by a Humanoid’s Upper Body. Active Media Technology — 9th International Conference (AMT
2013), 8210, 84–95.
@inproceedings{mohammad2013correspondence,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Tackling the Correspondence Problem --- Closed-Form Solution for
Gesture Imitation by a Humanoid's Upper Body},
booktitle = {Active Media Technology --- 9th International Conference (AMT
2013)},
series = {Lecture Notes in Computer Science},
volume = {8210},
pages = {84--95},
publisher = {Springer},
address = {Maebashi, Japan},
year = {2013},
doi = {10.1007/978-3-319-02750-0_9}
}
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2013). Approximately Recurring Motif Discovery Using Shift Density
Estimation. Recent Trends in Applied Artificial Intelligence — 26th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2013), 7906, 141–150.
@inproceedings{mohammad2013armd,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Approximately Recurring Motif Discovery Using Shift Density
Estimation},
booktitle = {Recent Trends in Applied Artificial Intelligence --- 26th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2013)},
series = {Lecture Notes in Computer Science},
volume = {7906},
pages = {141--150},
publisher = {Springer},
address = {Amsterdam, The Netherlands},
year = {2013},
doi = {10.1007/978-3-642-38577-3_15}
}
Approximately Recurring Motif (ARM) discovery is the problem of
finding unknown patterns that appear frequently in real valued
timeseries. In this paper, we propose a novel algorithm for
solving this problem that can achieve performance comparable with
the most accurate algorithms to solve this problem with a speed
comparable to the fastest ones. The main idea behind the proposed
algorithm is to convert the problem of ARM discovery into a
density estimation problem in the single dimensionality
shift-space (rather than in the original time-series space). This
makes the algorithm more robust to short noise bursts that can
dramatically affect the performance of most available algorithms.
The paper also reports the results of applying the proposed
algorithm to synthetic and real-world datasets.
Robotics & HRI
Mohammad, Y. F. O., Nishida, T., & Nakazawa, A. (2013). Arm Pose Copying for Humanoid Robots. IEEE International Conference on Robotics and Biomimetics
(ROBIO 2013), 897–904.
@inproceedings{mohammad2013armpose,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki and Nakazawa, Atsushi},
title = {Arm Pose Copying for Humanoid Robots},
booktitle = {IEEE International Conference on Robotics and Biomimetics
(ROBIO 2013)},
pages = {897--904},
publisher = {IEEE},
address = {Shenzhen, China},
year = {2013},
doi = {10.1109/ROBIO.2013.6739576}
}
Learning by imitation is becoming increasingly important for
teaching humanoid robots new skills. The simplest form of
imitation is behavior copying in which the robot is minimizing
the difference between its perceived motion and that of the
imitated agent. One problem that must be solved even in this
simplest of all imitation tasks is calculating the learner’s pose
corresponding to the perceived pose of the agent it is imitating.
This paper presents a general framework for solving this problem
in closed form for the arms of a generalized humanoid robot of
which most available humanoids are special cases. The paper also
reports the evaluation of the proposed system for real and
simulated robots.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2013). Learning Sensorimotor Concepts Without Reinforcement. Lifelong Machine Learning — 2013 AAAI Spring Symposium, SS-13-05.
@inproceedings{mohammad2013sensorimotor,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Sensorimotor Concepts Without Reinforcement},
booktitle = {Lifelong Machine Learning --- 2013 AAAI Spring Symposium},
series = {AAAI Technical Report},
volume = {SS-13-05},
publisher = {AAAI},
address = {Palo Alto, CA, USA},
year = {2013}
}
Agents engaged in lifelong learning can benefit from the ability
to acquire new concepts from continuous interaction with objects
in their environments which is a ubiquitous ability in humans.
This paper advocates the use of sensorimotor concepts that
combine perceptual and actuation patterns. Related
representations to sensorimotor concepts are Predictive State
Representation in dynamical systems, Affordance Based Concepts in
language and Skills in reinforcement learning. The paper proposes
a system for learning generalized sensorimotor concepts from
unsegmented interactions between the agent and the objects in its
environment that works in continuous action and observation
spaces and in the same time require no reinforcement signals. A
proof-of-concept experiment with the proposed system on a
simulated e-puck robot is reported to support the applicability
of the proposed approach.
Robotics & HRI
Lala, D., Nishida, T., & Mohammad, Y. (2013). Unsupervised Gesture Recognition System for Learning Manipulative
Actions in Virtual Basketball. Proceedings of the 1st International Conference on Human-Agent
Interaction (IHAI 2013).
@inproceedings{lala2013gesture,
keywords = {conference},
focus = {robotics},
author = {Lala, Divesh and Nishida, Toyoaki and Mohammad, Yasser},
title = {Unsupervised Gesture Recognition System for Learning Manipulative
Actions in Virtual Basketball},
booktitle = {Proceedings of the 1st International Conference on Human-Agent
Interaction (iHAI 2013)},
year = {2013}
}
Robotics & HRI
Mohammad, Y. F. O., Ohmoto, Y., & Nishida, T. (2012). Common Sensorimotor Representation for Self-Initiated Imitation
Learning. Advanced Research in Applied Artificial Intelligence — 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012), 7345, 381–390.
@inproceedings{mohammad2012commonsensorimotor,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Ohmoto, Yoshimasa and Nishida, Toyoaki},
title = {Common Sensorimotor Representation for Self-Initiated Imitation
Learning},
booktitle = {Advanced Research in Applied Artificial Intelligence --- 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012)},
series = {Lecture Notes in Computer Science},
volume = {7345},
pages = {381--390},
publisher = {Springer},
address = {Dalian, China},
year = {2012},
doi = {10.1007/978-3-642-31087-4_40}
}
Internal representation is an important design decision in any
imitation learning system. Actions and perceptual spaces where
separate in classical AI due to standard sense-process-act loop.
Recently another representation that combines the two spaces into
what we call a common sensorimotor space was inspired by the
discovery of mirror neurons in animals and humans. The
justification of this move is usually biological plausibility.
This paper reports a series of experiments comparing these two
alternatives for self-initiated imitation tasks. The results of
these experiments show that using a common sensorimotor
representation allows the system to achieve higher accuracy and
sensitivity. This is shown to be true (for our scenarios) even
when the dimensionality of the common sensorimotor representation
is higher than the dimensionality of the separate perceptual
space. It also allows for an easier behavior generation mechanism
and ensures reproducibility of learned behavior by the learner.
Mohammad, Y., & Nishida, T. (2012). Unsupervised Discovery of Basic Human Actions from Activity
Recording Datasets. IEEE/SICE International Symposium on System Integration (SII
2012), 402–409.
@inproceedings{mohammad2012basichuman,
keywords = {conference},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Unsupervised Discovery of Basic Human Actions from Activity
Recording Datasets},
booktitle = {IEEE/SICE International Symposium on System Integration (SII
2012)},
pages = {402--409},
publisher = {IEEE},
address = {Fukuoka, Japan},
year = {2012},
doi = {10.1109/SII.2012.6426960}
}
Human Behavior Understanding (HBU) is a major challenge facing
intelligent agents. Most approaches to solve this problem assume
a recognition/detection context in which the agent/robot tries to
match the perceived behavior to one or more predefined motion
patterns (e.g. walking, running etc). A more challenging problem
is discovering these motion patterns without apriori assumption
about the motions in the data, their duration or their numbers.
This paper proposes the utilization of a novel motif discovery
algorithm based on the exact MK algorithm to discover basic
actions in activity records. The proposed system was evaluated on
real records of full body motions and is shown in this paper to
achieve high accuracy compared with a recently proposed motif
discovery algorithm applied to the same dataset.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2012). Self-Initiated Imitation Learning: Discovering What to Imitate. 12th International Conference on Control, Automation And
Systems (ICCAS 2012), 726–732.
@inproceedings{mohammad2012selfinitiated,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Self-Initiated Imitation Learning: Discovering What to Imitate},
booktitle = {12th International Conference on Control, Automation and
Systems (ICCAS 2012)},
pages = {726--732},
publisher = {IEEE},
address = {Jeju, South Korea},
year = {2012}
}
Imitation learning is an important area in robotics and agents
research because it provides an easy way for robot programming
and also a bootstrapping technique for social learning. Available
learning by imitation systems implicitly or explicitly assume
that the boundaries of the actions to be imitated are set by the
demonstrator and that the robot is in some imitation mode during
the whole interaction session. A less researched area is
self-initiated imitation in which the robot needs to decide for
itself what to imitate from another imitatee that may not be
actively involved in the demonstration process. In this paper, we
propose a self-initiated imitation engine based on combining
techniques from time-series analysis and causality discovery. The
paper also reports a series of proof of concept experiments using
simulated and real robots. These evaluations show that the
proposed approach is capable of discovering important patterns of
behavior during the interaction session and faithfully reproduces
them.
ML & Time-Series
Mohammad, Y. F. O., Ohmoto, Y., & Nishida, T. (2012). CPMD: A Matlab Toolbox for Change Point and Constrained Motif
Discovery. Advanced Research in Applied Artificial Intelligence — 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012), 7345, 114–123.
@inproceedings{mohammad2012cpmd,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Ohmoto, Yoshimasa and Nishida, Toyoaki},
title = {{CPMD}: A Matlab Toolbox for Change Point and Constrained Motif
Discovery},
booktitle = {Advanced Research in Applied Artificial Intelligence --- 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012)},
series = {Lecture Notes in Computer Science},
volume = {7345},
pages = {114--123},
publisher = {Springer},
address = {Dalian, China},
year = {2012},
doi = {10.1007/978-3-642-31087-4_13}
}
Change Point Discovery (CPD) and Constrained Motif Discovery
(CMD) are two essential problems in data mining with applications
in many fields including robotics, economics, neuroscience and
other fields. In this paper, we show that these two problems are
related and report the development of a MATLAB Toolbox (CPMD)
that encapsulates several useful algorithms including new
variants to solve these two related problems. The Toolbox is then
used to study the effect of distance function choice in CPD.
ML & Time-Series
Mohammad, Y. F. O., Ohmoto, Y., & Nishida, T. (2012). G-SteX: Greedy Stem Extension for Free-Length Constrained Motif
Discovery. Advanced Research in Applied Artificial Intelligence — 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012), 7345, 417–426.
@inproceedings{mohammad2012gstex,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Ohmoto, Yoshimasa and Nishida, Toyoaki},
title = {{G-SteX}: Greedy Stem Extension for Free-Length Constrained Motif
Discovery},
booktitle = {Advanced Research in Applied Artificial Intelligence --- 25th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2012)},
series = {Lecture Notes in Computer Science},
volume = {7345},
pages = {417--426},
publisher = {Springer},
address = {Dalian, China},
year = {2012},
doi = {10.1007/978-3-642-31087-4_44}
}
Most available motif discovery algorithms in real-valued time
series find approximately recurring patterns of a known length
without any prior information about their locations or shapes. In
this paper, a new motif discovery algorithm is proposed that has
the advantage of requiring no upper limit on the motif length.
The proposed algorithm can discover multiple motifs of multiple
lengths at once, and can achieve a better accuracy-speed balance
compared with a recently proposed motif discovery algorithm. We
then briefly report two successful applications of the proposed
algorithm to gesture discovery and robot motion pattern
discovery.
ML & Time-Series
Mohammad, Y., & Nishida, T. (2011). Discovering Causal Change Relationships Between Processes in
Complex Systems. IEEE/SICE International Symposium on System Integration (SII
2011), 12–17.
@inproceedings{mohammad2011causalchange,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Discovering Causal Change Relationships Between Processes in
Complex Systems},
booktitle = {IEEE/SICE International Symposium on System Integration (SII
2011)},
pages = {12--17},
publisher = {IEEE},
address = {Kyoto, Japan},
year = {2011}
}
Complex systems involve the interaction between many processes
that may or may not have causal relations to each other. In such
systems, discovering causal relations can provide significant
insights into the internals of the system and facilitate fault
discovery and recovery procedures. In this paper, we provide a
novel causality detection algorithm based on robust singular
spectrum transform that combines features of autoregressive
modeling and perturbation analysis. The proposed approach was
evaluated using both synthetic and real data and was shown to
provide superior performance to the standard linear
Granger-causality test. It also provides a natural way to detect
common causes that may give false positives in other causality
tests.
ML & Time-Series
Mohammad, Y., & Nishida, T. (2011). On Comparing SSA-Based Change Point Discovery Algorithms. IEEE/SICE International Symposium on System Integration (SII
2011), 938–945. Best Paper Award (Control).
IEEE SII 2011 Best Paper
@inproceedings{mohammad2011ssacomparison,
award = {IEEE SII 2011 Best Paper},
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {On Comparing {SSA}-Based Change Point Discovery Algorithms},
booktitle = {IEEE/SICE International Symposium on System Integration (SII
2011)},
pages = {938--945},
publisher = {IEEE},
address = {Kyoto, Japan},
year = {2011},
note = {Best Paper Award (Control)}
}
Change point discovery is an important problem in data mining
and industrial systems. Different approaches have been proposed
and some of the most promising approaches are based on singular
spectrum analysis (SSA). These algorithms have the advantages of
requiring no ad-hoc tuning for different types of signals and
having a built-in noise attenuation mechanism. In this paper we
try to unify these approaches and present a novel method for
comparing change point discovery algorithms. We then use the
proposed method to compare different SSA based change point
discovery algorithms. Even though we focused on comparing only
SSA based algorithms, the proposed metric applicable to any kind
of change point discovery algorithm and have the advantages of
requiring no localization steps, and being independent of any
predefined thresholds (unlike traditional metrics).
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Incremental Gesture Discovery for Interactive Robots. IEEE International Conference on Robotics and Biomimetics
(ROBIO 2010), 185–189.
@inproceedings{mohammad2010incremental,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Incremental Gesture Discovery for Interactive Robots},
booktitle = {IEEE International Conference on Robotics and Biomimetics
(ROBIO 2010)},
pages = {185--189},
publisher = {IEEE},
address = {Tianjin, China},
year = {2010},
doi = {10.1109/ROBIO.2010.5723324}
}
An incremental method for gesture discovery from continuous time
streams based on CMD algorithm is introduced and evaluated.
Results show that the method is as accurate as batch discovery
and that utilizing action streams is effective in removing false
negatives
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Learning Interaction Protocols Using Augmented Bayesian Networks
Applied to Guided Navigation. IEEE/RSJ International Conference on Intelligent Robots And
Systems (IROS 2010), 4119–4126.
@inproceedings{mohammad2010augmentedbayes,
keywords = {conference, important},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Interaction Protocols Using Augmented Bayesian Networks
Applied to Guided Navigation},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2010)},
pages = {4119--4126},
publisher = {IEEE},
address = {Taipei, Taiwan},
year = {2010},
doi = {10.1109/IROS.2010.5651719}
}
Research in robot navigation usually concentrates on
implementing navigation algorithms that allow the robot to
navigate without human aid. In many real world situations, it is
desirable that the robot is able to understand natural gestures
from its user or partner and use this understanding to guide its
navigation. Some algorithms already exist for learning natural
gestures and/or their associated actions but most of these
systems does not allow the robot to automatically generate the
associated controller that allows it to actually navigate in the
real environment. Furthermore, a technique is needed to combine
the gestures/actions learned from interacting with multiple users
or partners. This paper resolves these two issues and provides a
complete system that allows the robot to learn interaction
protocols and act upon them using only unsupervised learning
techniques and enables it to combine the protocols learned from
multiple users/partners. The proposed approach is general and can
be applied to other interactive tasks as well. This paper also
provides a real world experiment involving 18 subjects and 72
sessions that supports the ability of the proposed system to
learn the needed gestures and to improve its knowledge of
different gestures and their associations to actions over time.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Learning Spontaneous Nonverbal Behavior Using a Three Layers
Hierarchy. Proceedings of the 10th WSEAS International Conference On
Applied Computer Science, 430–435.
@inproceedings{mohammad2010threelayers,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Learning Spontaneous Nonverbal Behavior Using a Three Layers
Hierarchy},
booktitle = {Proceedings of the 10th WSEAS International Conference on
Applied Computer Science},
pages = {430--435},
address = {Iwate, Japan},
year = {2010}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2010). Down-Up-Down Behavior Generation for Interactive Robots. Trends in Applied Intelligent Systems — 23rd International
Conference on Industrial, Engineering and Other Applications Of
Applied Intelligent Systems (IEA/AIE 2010), 6096, 92–101.
@inproceedings{mohammad2010downupdown,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Down-Up-Down Behavior Generation for Interactive Robots},
booktitle = {Trends in Applied Intelligent Systems --- 23rd International
Conference on Industrial, Engineering and Other Applications of
Applied Intelligent Systems (IEA/AIE 2010)},
series = {Lecture Notes in Computer Science},
volume = {6096},
pages = {92--101},
publisher = {Springer},
address = {Cordoba, Spain},
year = {2010},
doi = {10.1007/978-3-642-13022-9_10}
}
Behavior generation in humans and animals usually employs a
combination of bottom-up and top-down patterns. Most available
robotic architectures utilize either bottom-up or top-down
activation including hybrid architectures. In this paper, we
propose a behavior generation mechanism that can seamlessly
combine these two strategies. One of the main advantages of the
proposed approach is that it can naturally combine both bottom-up
and top-down behavior generation mechanisms which can produce
more natural behavior. This is achieved by utilizing results from
the theory of simulation in neuroscience which tries to model the
mechanism used in human infants to develop a theory of mind. The
proposed approach was tested in modeling spontaneous gaze control
during natural face to face interactions and provided more
natural, human-like behavior compared with a state-of-the-art
gaze controller that utilized a bottom-up approach.
Robotics & HRI
Mohammad, Y. F. O., Nishida, T., & Okada, S. (2009). Unsupervised Simultaneous Learning of Gestures, Actions and Their
Associations for Human-Robot Interaction. IEEE/RSJ International Conference on Intelligent Robots And
Systems (IROS 2009), 2537–2544.
@inproceedings{mohammad2009simultaneous,
keywords = {conference, important},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki and Okada, Shogo},
title = {Unsupervised Simultaneous Learning of Gestures, Actions and Their
Associations for Human-Robot Interaction},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2009)},
pages = {2537--2544},
publisher = {IEEE},
address = {St. Louis, MO, USA},
year = {2009},
doi = {10.1109/IROS.2009.5353987}
}
To enable free natural communication between a human operator
and a robot three problems must be faced: Firstly the robot have
to know the actions it can do in the world. Secondly the robot
must be able to learn the patterns in the perceived behavior of
its operator that correspond to commands. Finally the robot needs
to know when to execute a specific action based on its perception
of the operator’s behavior. In this paper we are interested in
free hand gestures as the commanding channel. The most
restrictive solution to the aforementioned three problems is to
fix the action space (pre-programmed actions), fix the command
space (predefined gestures), and fix action-command relation
(fixed gesture meanings).Learning by demonstration can be viewed
as a technique to relax the first restriction by learning the
action space. Gesture interpretation can be viewed as a technique
to relax the second restriction by learning the command space.
Reinforcement learning can be viewed as a technique for relaxing
the third restriction by learning action-command associations
(policy). In this paper we propose a novel technique that allows
the robot to solve these three problems together learning the
action space, the command space, and their relations by just
another robot operated by a human operator. The main technical
contribution of this paper is the introduction of a novel
algorithm that allows the robot to segment and discover patterns
in its perceived signals without any prior knowledge of the
number of different patterns, their occurrences or lengths. The
second contribution is using a Ganger-Causality based test to
limit the search space for actions and commands utilizing their
relations and taking into account the autonomy level of the
robot. The paper also presents a feasibility study in which the
learning robot was able to predict actor’s behavior with 95.2
after monitoring a single interaction between a novice operator
and a WOZ operated robot representing the actor.
ML & Time-Series
Mohammad, Y. F. O., & Nishida, T. (2009). Robust Singular Spectrum Transform. Next-Generation Applied Intelligence — 22nd International
Conference on Industrial, Engineering and Other Applications Of
Applied Intelligent Systems (IEA/AIE 2009), 5579, 123–132. Best Paper Award.
IEA/AIE 2009 Best Paper
@inproceedings{mohammad2009rsst,
award = {IEA/AIE 2009 Best Paper},
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Robust Singular Spectrum Transform},
booktitle = {Next-Generation Applied Intelligence --- 22nd International
Conference on Industrial, Engineering and Other Applications of
Applied Intelligent Systems (IEA/AIE 2009)},
series = {Lecture Notes in Computer Science},
volume = {5579},
pages = {123--132},
publisher = {Springer},
address = {Tainan, Taiwan},
year = {2009},
doi = {10.1007/978-3-642-02568-6_13},
note = {Best Paper Award}
}
Change Point Discovery is a basic algorithm needed in many time
series mining applications including rule discovery, motif
discovery, casual analysis, etc. Several techniques for change
point discovery have been suggested including wavelet analysis,
cosine transforms, CUMSUM, and Singular Spectrum Transform. Of
these methods Singular Spectrum Transform (SST) have received
much attention because of its generality and because it does not
require ad-hoc adjustment for every time series. In this paper we
show that traditional SST suffers from two major problems: the
need to specify five parameters and the rapid reduction in the
specificity with increased noise levels. In this paper we define
the Robust Singular Spectrum Transform (RSST) that alleviates
both of these problems and compare it to RSST using different
synthetic and real-world data series.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2009). Measuring Naturalness During Close Encounters Using Physiological
Signal Processing. Next-Generation Applied Intelligence — 22nd International
Conference on Industrial, Engineering and Other Applications Of
Applied Intelligent Systems (IEA/AIE 2009), 5579, 281–290.
@inproceedings{mohammad2009naturalness,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Measuring Naturalness During Close Encounters Using Physiological
Signal Processing},
booktitle = {Next-Generation Applied Intelligence --- 22nd International
Conference on Industrial, Engineering and Other Applications of
Applied Intelligent Systems (IEA/AIE 2009)},
series = {Lecture Notes in Computer Science},
volume = {5579},
pages = {281--290},
publisher = {Springer},
address = {Tainan, Taiwan},
year = {2009},
doi = {10.1007/978-3-642-02568-6_29}
}
Many researchers in the HRI and ECA domains try to build robots
and agents that exhibit human-like behavior in real-world close
encounter situations. One major requirement for comparing such
robots and agents is to have an objective quantitative metric for
measuring naturalness in various kinds of interactions. Some
researchers have already suggested techniques for measuring
stress level, awareness etc using physiological signals like GSR
and BVP. One problem of available techniques is that they are
only tested with extreme situations and cannot according to the
analysis provided in this paper distinguish the response of human
subjects in natural interaction situations. One other problem of
the available techniques is that most of them require calibration
and some times ad-hoc adjustment for every subject. This paper
explores the usefulness of various kinds of physiological signals
and statistics in distinguishing natural and unnatural partner
behavior in a close encounter situation. The paper also explores
the usefulness of these statistics in various time slots of the
interaction. Based on this analysis a regressor was designed to
measure naturalness in close encounter situations and was
evaluated using human-human and human-robot interactions and
shown to achieve 92.5 situations.
ML & Time-Series
Mohammad, Y., & Nishida, T. (2009). Change Point Detection Using Robust Singular Spectrum Transform
Applied to Mining Human-Human Interaction Records. International Symposium on Data Mining and Statistical Science.
@inproceedings{mohammad2009changepoint,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Change Point Detection Using Robust Singular Spectrum Transform
Applied to Mining Human-Human Interaction Records},
booktitle = {International Symposium on Data Mining and Statistical Science},
address = {Japan},
year = {2009}
}
Change Point Discovery is a basic algorithm needed in many time
series mining applications including rule discovery, motif
discovery, casual analysis, etc. Several techniques for change
point discovery have been suggested including wavelet analysis,
cosine transforms, CUMSUM, and Singular Spectrum Transform. Of
these methods Singular Spectrum Transform (SST) have received
much attention because of its generality and because it does not
require ad-hoc adjustment for every time series. In this paper we
show that traditional SST suffers from two major problems: 1. The
need to specify five parameters some of which are very hard to
select even if domain knowledge is available. 2. The specificity
of the transform degrades rapidly with increased noise levels
specially when the background signal of the time series is zero
which is true for many human generated signals. The main
contributions of this paper are: 1. Defining the RSST transform
which requires the specification of only two parameters that can
be decided easily based on domain knowledge or visualization, and
achieves better performance than traditional Singular Spectrum
Transform (SST) in noisy environments. The proposed algorithm is
compared to SST and analyzed extensively using several synthetic
and real world time series. 2. Applying the algorithm to achieve
8 folds speedup in selecting candidate windows for a motif
discovery algorithm 3. Using RSST to define an objective
physiological measure of interaction ’naturalness’ in human-human
explanation scenarios.
Robotics & HRI
Mohammad, Y., Xu, Y., Matsumura, K., & Nishida, T. (2008). The H3R Explanation Corpus: Human-Human and Base Human-Robot
Interaction Dataset. The Fourth International Conference on Intelligent Sensors,
Sensor Networks and Information Processing (ISSNIP 2008), 201–206.
@inproceedings{mohammad2008h3r,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Xu, Yong and Matsumura, Kenichi and Nishida, Toyoaki},
title = {The {H3R} Explanation Corpus: Human-Human and Base Human-Robot
Interaction Dataset},
booktitle = {The Fourth International Conference on Intelligent Sensors,
Sensor Networks and Information Processing (ISSNIP 2008)},
pages = {201--206},
publisher = {IEEE},
address = {Sydney, Australia},
year = {2008}
}
Natural interaction between humanoid robots and humans is one of
the major goals of the HRI field. Two major requirements for
advancing this direction of research are the availability of
human–human and base human–robot interaction datasets for
training and evaluation purposes and the availability of general
agreed upon objective metrics for judging the performance of
proposed robots and algorithms. In this paper we report details
of the H 3R Explanation Corpus dataset of human–human and base
human–robot interactions in assembly/disassembly explanation
scenarios that combines five kinds of data: video, audio, motion
tracking, subjective, and physiological data. 44 subjects and 66
sessions were conducted during this experiment. The corpus
contains 22 natural Human–Human interactions, 22 un-natural
Human–Human interactions, and 22 baseline Human-Robot
interactions. To our best knowledge this is the first database
that combines these five data types and three types of
interactions. The paper also reports the first usage of this
explanation corpus to compare subjective and physiological
evaluations of various dimensions of listener’s behavior.
ML & Time-Series
Mohammad, Y., & Nishida, T. (2008). Constrained Motif Discovery. International Symposium on Data Mining and Statistical Science
(DMSS 2008), 16–19.
@inproceedings{mohammad2008dmss,
keywords = {conference},
focus = {timeseries},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Constrained Motif Discovery},
booktitle = {International Symposium on Data Mining and Statistical Science
(DMSS 2008)},
pages = {16--19},
address = {Tokyo, Japan},
year = {2008}
}
The goal of motif discovery algorithms is to efficiently find
unknown recurring patterns in time series. Most available
algorithms cannot utilize domain knowledge in any way which
results in quadratic or at least super-linear time and space
complexity. In this paper we define the Constrained Motif
Discovery problem which enables utilization of domain knowledge
into the motif discovery process. The paper then provides two
algorithms called MCFull and MCInc for efficiently solving the
constrained motif discovery problem. We also show that most
unconstrained motif discovery problems be converted into
constrained ones using a change-point detection algorithm. A
novel change-point detection algorithm called the Robust Singular
Spectrum Transform (RSST) is then introduced and compared to
traditional Singular Spectrum Transform using synthetic and
real-world data sets. The results show that RSST achieves higher
specificity and is more adequate for finding constraints to
convert unconstrained motif discovery problems to constrained
ones that can be solved using MCFull and MCInc. We then compare
the combination of RSST and MCFull or MCInc with two
state-of-the-art motif discovery algorithms on a large set of
synthetic time series. The results show that the proposed
algorithms provided four to ten folds increase in speed compared
the unconstrained motif discovery algorithms studied without any
loss of accuracy. One of the proposed constrained motif discovery
algorithms (MCFull) is then used along with the RSST algorithm in
a real world human-robot interaction experiment to enable the
robot to learn free hand gestures, actions, and their
associations by humans and other robots interacting.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2008). Human Adaptation to a Miniature Robot: Precursors of Mutual
Adaptation. The 17th IEEE International Symposium on Robot and Human
Interactive Communication (RO-MAN 2008), 124–129.
@inproceedings{mohammad2008adaptation,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Human Adaptation to a Miniature Robot: Precursors of Mutual
Adaptation},
booktitle = {The 17th IEEE International Symposium on Robot and Human
Interactive Communication (RO-MAN 2008)},
pages = {124--129},
publisher = {IEEE},
address = {Munich, Germany},
year = {2008},
doi = {10.1109/ROMAN.2008.4600654}
}
Mutual adaptation is an important phenomenon in human-human
communications. Traditionally HRI research were more interested
in investigating adaptation of the robot to the human using
machine learning techniques but the possibility of utilizing the
natural ability of humans to adapt to other humans and artifacts
including robots is recently becoming more attractive. This paper
presents some of the results from an experiment conducted to
investigate the interaction patterns and effectiveness of motion
cues as a feedback modality between a human operator and a
miniature robot in a confined collaborative navigation task. The
results presented in this paper show evidence of human adaptation
to the robot and moreover suggest that the adaptation rate is not
constant or continuous in time but is discontinuous and
nonlinear. The results also show evidence of a starting stage
before the adaptation with duration dependent on the expectations
of the human regarding the capabilities of the robot in the given
task. The paper investigates how to utilize these and related
findings for building robots capable of not only adapting to
human operators but can also help those operators adapt to them.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2008). Getting Feedback from a Miniature Robot. IEEE International Conference on Information and Automation
(ICIA 2008), 941–947.
@inproceedings{mohammad2008gettingfeedback,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Getting Feedback from a Miniature Robot},
booktitle = {IEEE International Conference on Information and Automation
(ICIA 2008)},
pages = {941--947},
publisher = {IEEE},
address = {Changsha, China},
year = {2008}
}
The HRI field of research has gained much attention recently
because of the expected importance of well designed interaction
modalities with social robots. To achieve natural interaction
between the robot and the human, a feedback mechanism from the
robot to the human needs to be designed that allows the robot to
express its internal state to the human in a natural way. Verbal
and nonverbal feedback from humanoid robots or humanoid robotic
heads have been widely studied but there is little comparable
research about the possible feedback mechanisms of non-humanoid
and especially miniature robots. In this paper a comparison
between using verbal feedback and motion cues is conducted. The
results of the experiment showed that there is no significant
difference in the task completion accuracy and time or in the
feeling of naturalness between these two modalities and there is
a statistically significant improvement when using any of them
compared with the no-feedback (control) case. Moreover the
subjects selected the motion cues feedback mechanism more
frequently as the preferred feedback modality for them.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2008). Reactive Gaze Control for Natural Human-Robot Interactions. IEEE Conference on Robotics, Automation and Mechatronics (RAM
2008), 47–54.
@inproceedings{mohammad2008reactivegaze,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Reactive Gaze Control for Natural Human-Robot Interactions},
booktitle = {IEEE Conference on Robotics, Automation and Mechatronics (RAM
2008)},
pages = {47--54},
publisher = {IEEE},
address = {Chengdu, China},
year = {2008},
doi = {10.1109/RAMECH.2008.4681417}
}
Nonverbal behavior during human-human close encounters is
critical to the accomplishment of natural interaction. For this
reason, humanoid robots trying to achieve natural interactions
with humans should be able to understand and synthesis nonverbal
behavior in a way that mimics the human use of it. One of the
most important situations during natural human-robot interactions
is the explanation scenario in which the human is explaining a
task to the robot using natural verbal and nonverbal behavior.
This situation occurs frequently in many HRI applications and is
critical to the success of the Robots as Knowledge Media project
suggested by the authors. In this paper the implementation of a
humanoid robot that can show human like gaze control during
explanation settings based only on reactive processing is
presented. The software of the robot is based on the EICA
architecture designed to combine autonomy with interactivity in
the lowest level of the system. The details of the implementation
and analysis of the naturalness of behavior and the effect of
noisy input is presented in this paper.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2008). A Cross-Platform Robotic Architecture for Autonomous Interactive
Robots. New Frontiers in Applied Artificial Intelligence — 21st
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2008), 5027, 108–118. Best Paper Award.
IEA/AIE 2008 Best Paper
@inproceedings{mohammad2008crossplatform,
award = {IEA/AIE 2008 Best Paper},
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {A Cross-Platform Robotic Architecture for Autonomous Interactive
Robots},
booktitle = {New Frontiers in Applied Artificial Intelligence --- 21st
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2008)},
series = {Lecture Notes in Computer Science},
volume = {5027},
pages = {108--118},
publisher = {Springer},
address = {Wroclaw, Poland},
year = {2008},
doi = {10.1007/978-3-540-69052-8_12},
note = {Best Paper Award}
}
HRI is a growing field of research that targets the development
of robots which are easy to operate, more engaging and more
entertaining. Most robotic architectures available including
reactive, deliberative, and hybrid architectures were developed
with autonomous robots in mind. Although robots developed for HRI
are usually autonomous, their autonomy is combined with
interactivity which adds more challenges on the design of the
robotic architectures supporting them. Among these challenges are
the flexibility in combining deliberative and interactive
processes, low level attention focusing, and flexible action
integration. This paper reports the lowest level of specification
of a new cross-platform robotic architecture for HRI applications
called EICA (The Embodied Interactive Control Architecture) that
was designed to meet those challenges emphasizing how low level
attention focusing and action integration are implemented. Two of
the main advantages of the proposed architecture are real-time
architectural modifiability and supporting adaptability and
learning through interactions between processes. The paper
describes in details the L 0EICA based design of two gaze
controllers that uses the aforementioned properties of the
architecture to achieve human-like nonverbal behavior during
explanation scenarios. Robotic Architectures Action Integration
HRI Gaze Control
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2008). Two Layers Action Integration for HRI — Action Integration with
Attention Focusing for Interactive Robots. Proceedings of the Fifth International Conference On
Informatics in Control, Automation and Robotics (ICINCO 2008), 41–48.
@inproceedings{mohammad2008twolayers,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Two Layers Action Integration for {HRI} --- Action Integration with
Attention Focusing for Interactive Robots},
booktitle = {Proceedings of the Fifth International Conference on
Informatics in Control, Automation and Robotics (ICINCO 2008)},
pages = {41--48},
publisher = {INSTICC Press},
address = {Funchal, Madeira, Portugal},
year = {2008}
}
Robotics & HRI
Mohammad, Y., & Nishida, T. (2008). Natural Listening for a Humanoid Robot. Proceedings of the International Conference on Informatics
Education and Research for Knowledge-Circulating Society (ICKS
2008), 153–156.
@inproceedings{mohammad2008naturallistening,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Natural Listening for a Humanoid Robot},
booktitle = {Proceedings of the International Conference on Informatics
Education and Research for Knowledge-Circulating Society (ICKS
2008)},
pages = {153--156},
publisher = {IEEE},
address = {Kyoto, Japan},
year = {2008}
}
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2007). TalkBack: Feedback from a Miniature Robot. AI 2007: Advances in Artificial Intelligence — 20th
Australian Joint Conference on Artificial Intelligence, 4830, 357–366.
@inproceedings{mohammad2007talkback,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {{TalkBack}: Feedback from a Miniature Robot},
booktitle = {AI 2007: Advances in Artificial Intelligence --- 20th
Australian Joint Conference on Artificial Intelligence},
series = {Lecture Notes in Computer Science},
volume = {4830},
pages = {357--366},
publisher = {Springer},
address = {Gold Coast, Australia},
year = {2007},
doi = {10.1007/978-3-540-76928-6_37}
}
A prerequisite of any successful social robot is the ability to
express its internal state and intention to humans in a natural
way. Many researchers studied verbal and nonverbal feedback from
humanoid robots or humanoid robotic heads but there is little
research about the possible feedback mechanisms of non-humanoid
and especially miniature robots. The TalkBack experiment is a
trial to fill this gap by investigating the effectiveness of
using motion cues as a feedback mechanism and comparing it to
verbal feedback. The results of the experiment showed that there
is no significant difference in the task completion accuracy and
time or in the feeling of naturalness between these two
modalities and there is a statistically significant improvement
when using any of them compared with the no-feedback case.
Moreover the subjects selected the motion cues feedback mechanism
more frequently as the preferred feedback modality for them.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2007). Intention Through Interaction: Toward Mutual Intention in Real
World Interactions. New Trends in Applied Artificial Intelligence — 20th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2007), 4570, 115–125.
@inproceedings{mohammad2007intention,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Intention Through Interaction: Toward Mutual Intention in Real
World Interactions},
booktitle = {New Trends in Applied Artificial Intelligence --- 20th
International Conference on Industrial, Engineering and Other
Applications of Applied Intelligent Systems (IEA/AIE 2007)},
series = {Lecture Notes in Computer Science},
volume = {4570},
pages = {115--125},
publisher = {Springer},
address = {Kyoto, Japan},
year = {2007},
doi = {10.1007/978-3-540-73325-6_12}
}
Human-Artifact interaction in real world situations is currently
an active area of research due to the importance foreseen of the
social capabilities of near future robots and other intelligent
artifacts in integrating them into the human society. In this
paper a new paradigm for mutual intention in human-artifact
interactions based on the embodied computing paradigm called is
introduced with theoretical analysis of its relation to the
embodiment framework. As examples of the practical use of the
framework to replace traditional symbolic based intention
understanding systems, the authors’ preliminary work in a
real-world agent architecture (IECA) and a natural drawing
environment (NaturalDraw) is briefed.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2007). NaturalDraw: Interactive Perception Based Drawing for Everyone. Proceedings of the 12th International Conference on Intelligent
User Interfaces (IUI 2007), 251–260.
@inproceedings{mohammad2007naturaldraw,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {{NaturalDraw}: Interactive Perception Based Drawing for Everyone},
booktitle = {Proceedings of the 12th International Conference on Intelligent
User Interfaces (IUI 2007)},
pages = {251--260},
publisher = {ACM},
address = {Honolulu, HI, USA},
year = {2007},
doi = {10.1145/1216295.1216340}
}
Robotics & HRI
Mohammad, Y., Ohya, T., Hiramatsu, T., Sumi, Y., & Nishida, T. (2007). Embodiment of Knowledge into the Interaction and Physical Domains
Using Robots. International Conference on Control, Automation and Systems
(ICCAS 2007), 737–744.
@inproceedings{mohammad2007embodiment,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Ohya, Taku and Hiramatsu, Tatsuya and Sumi, Yasuyuki and Nishida, Toyoaki},
title = {Embodiment of Knowledge into the Interaction and Physical Domains
Using Robots},
booktitle = {International Conference on Control, Automation and Systems
(ICCAS 2007)},
pages = {737--744},
address = {Seoul, South Korea},
year = {2007}
}
Robots can do more for the human society than wandering around
collecting garbage, entertaining children and playing soccer. In
this paper we present an ongoing research toward the realization
of the robot as an embodied knowledge media. In this framework,
the robot is used to extract knowledge from expert humans through
natural interactions, and then to present this knowledge to other
humans who need it. The main benefits of using a robot rather
than a passive video camera are: 1) The natural interaction
between the human expert and the robot will reduce the stress and
cognitive load on the human side which allows generation of
better videos. 2) The robot will be more effective in extracting
useful knowledge by utilizing interaction events to generate
embodied knowledge contents in the form of conversation quanta
that embody the videos both in the interaction context and in the
physical situation. 3) The robot can present knowledge
intelligently to other humans who need it, and can generate
better views of the knowledge based on the embodied conversation
quanta it learned from interacting with the expert and on the
natural interaction context and physical context of the current
interaction situation. In This paper, the robot as knowledge
media theoretical framework will be presented with introduction
to the ongoing research conducted in our laboratory toward
realizing a humanoid robot that can realize it detailing the past
and current contributions and proposing the future research map
for this important area of robotic applications. The
implementation architecture of the robot based on our proposed
EICA system will also be presented with emphasize on the
knowledge acquisition behavior of the robot.
Robotics & HRI
Mohammad, Y., Harb, H. M., Mahdy, Y. B., & Mohammed, M. M. (2003). Overcoming Kerberos Structural Limitations. 38th Annual Conference of Statistics, Computer Sciences And
Operations Research, 172–187.
@inproceedings{mohammad2003kerberos,
keywords = {conference},
focus = {robotics},
author = {Mohammad, Yasser and Harb, Hany M. and Mahdy, Yousef B. and Mohammed, Montaser M.},
title = {Overcoming Kerberos Structural Limitations},
booktitle = {38th Annual Conference of Statistics, Computer Sciences and
Operations Research},
pages = {172--187},
address = {Cairo, Egypt},
year = {2003}
}
International Workshops
Automated Negotiation
Mohammad, Y. (2026). Reinforcement Learning for Automated Negotiation. The 8th Games, Agents, and Incentives Workshop (GAIW@AAMAS
2026).
@inproceedings{mohammad2026rlnegotiation,
keywords = {workshop},
focus = {negotiation,machinelearning},
author = {Mohammad, Yasser},
title = {Reinforcement Learning for Automated Negotiation},
booktitle = {The 8th Games, Agents, and Incentives Workshop (GAIW@AAMAS
2026)},
address = {Paphos, Cyprus},
year = {2026}
}
This tutorial introduces attendees to the problem of building
effective negotiation strategies using RL and MARL methods. After
providing the motivation for this problem, the tutorial presents
the needed theoretical background about automated negotiation and
reinforcement learning to appreciate different approaches. Based
on this background, the tutorial then introduces a unifying
framework and an accompanying open-source library (NegMAS-RL)
that can be used to represent most existing research in RL for
automated negotiation as well as new solutions not yet attempted.
This framework is then used to represent several recent advances
in applying RL to automated negotiation and motivate new
approaches. The attendees will then learn through a live
demonstration (with optional follow-along) how to use this
framework to represent, solve and evaluate the solution of a
specific problem of automated negotiation in supply chains using
the learnt framework.
Automated NegotiationMultiagent Systems
Mohammad, Y., Nakadai, S., & Greenwald, A. (2024). Automated Negotiation in Supply Chains Game. The First Workshop on Game AI Algorithms and Multi-Agent
Learning (GAAMAL@IJCAI 2024).
@inproceedings{mohammad2024scmgame,
keywords = {workshop},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and Nakadai, Shinji and Greenwald, Amy},
title = {Automated Negotiation in Supply Chains Game},
booktitle = {The First Workshop on Game AI Algorithms and Multi-Agent
Learning (GAAMAL@IJCAI 2024)},
address = {Jeju, South Korea},
year = {2024}
}
Automated NegotiationMultiagent Systems
Mohammad, Y. (2024). Tentative Acceptance Unique Offers Protocol for Automated
Negotiation. The 6th Games, Agents, and Incentives Workshop (GAIW@AAMAS
2024).
@inproceedings{mohammad2024tentative,
keywords = {workshop},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser},
title = {Tentative Acceptance Unique Offers Protocol for Automated
Negotiation},
booktitle = {The 6th Games, Agents, and Incentives Workshop (GAIW@AAMAS
2024)},
address = {Auckland, New Zealand},
year = {2024}
}
Automated NegotiationMultiagent Systems
Ninagawa, K., Mohammad, Y., & Greenwald, A. (2021). Baseline Strategies for the ANAC Automated Negotiation League. The 3rd Games, Agents, and Incentives Workshop (GAIW@AAMAS
2021).
@inproceedings{ninagawa2021baseline,
keywords = {workshop},
focus = {negotiation,multiagent},
author = {Ninagawa, Kotone and Mohammad, Yasser and Greenwald, Amy},
title = {Baseline Strategies for the {ANAC} Automated Negotiation League},
booktitle = {The 3rd Games, Agents, and Incentives Workshop (GAIW@AAMAS
2021)},
year = {2021}
}
Automated NegotiationMultiagent Systems
Mohammad, Y., Nakadai, S., & Greenwald, A. (2019). NegMAS: A Platform for Situated Negotiations. Twelfth International Workshop on Agent-Based Complex Automated
Negotiations (ACAN@IJCAI 2019), 57–75.
@inproceedings{mohammad2019negmassituated,
keywords = {workshop},
focus = {negotiation,multiagent},
author = {Mohammad, Yasser and Nakadai, Shinji and Greenwald, Amy},
title = {{NegMAS}: A Platform for Situated Negotiations},
booktitle = {Twelfth International Workshop on Agent-Based Complex Automated
Negotiations (ACAN@IJCAI 2019)},
pages = {57--75},
address = {Macau, China},
year = {2019}
}
Automated negotiations is attracting more interest from
researchers in recent years. Most research in this area focuses
on negotiation strategy formulation in preset scenarios where the
decisions of what to negotiate about, whom to negotiate with, and
on which issues are already set to the agent. Moreover, in most
cases, the utility functions employed are static and independent
between different negotiations. NegMAS (Negotiations Managed by
Agent Simulations/ Negotiation Multi-Agent System) was developed
to facilitate research and development of agents that negotiate
in more dynamic situations characterized by dynamic interrelated
utility functions with all negotiation related decisions managed
by the agent. This paper introduces NegMAS, its design and
overall structure, and evaluates its use in a simple situated
negotiations scenario.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2015). Cultural Difference in Back-Imitation’s Effect on the Perception of
Robot’s Imitative Performance. Cultural Robotics — First International Workshop (CR@RO-MAN
2015), 9549, 17–32.
@inproceedings{mohammad2015cultural,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {Cultural Difference in Back-Imitation's Effect on the Perception of
Robot's Imitative Performance},
booktitle = {Cultural Robotics --- First International Workshop (CR@RO-MAN
2015)},
series = {Lecture Notes in Computer Science},
volume = {9549},
pages = {17--32},
publisher = {Springer},
address = {Kobe, Japan},
year = {2015},
doi = {10.1007/978-3-319-42945-8_2}
}
Cultural differences have been documented in different aspects
of perception of robots as well as understanding of their
behavior. A different line of research in developmental
psychology have established a major role of imitation in cultural
transfer. This study is a preliminary cross cultural exploration
of the effect of imitating the robot (back imitation) on human’s
perception of the robot’s imitative skill. In previous research,
we have shown that engagement in back imitation with a NAO
humanoid robot, results in increased perception of the robot’s
imitative skill, motion human likeness, and willingness of future
interaction with the robot. This previous work mostly used
Japanese university students. In this paper, we report the
results of conducting the same study with subjects of two
cultures: Japanese and Egyptian university students. The first
finding of the study is that the two cultures have widely
different expectations of the robot and interaction with it and
that some of these differences are significantly reduced after
the interaction. The second finding is that Japanese students
tended to attribute higher imitation skill and human likeness to
the robot they imitated while Egyptian students did not show such
tendency. The paper discusses these findings in light of known
differences between the two cultures and analyzes the role of
expectation in the differences found.
Nishida, T., Nakazawa, A., Ohmoto, Y., Nitschke, C., Mohammad, Y. F. O., Thovutikul, S., Lala, D., Abe, M., & Ookaki, T. (2015). Synthetic Evidential Study as Primordial Soup of Conversation. Databases in Networked Information Systems — 10th
International Workshop (DNIS 2015), 8999, 74–83.
@inproceedings{nishida2015primordial,
keywords = {workshop},
author = {Nishida, Toyoaki and Nakazawa, Atsushi and Ohmoto, Yoshimasa and Nitschke, Christian and Mohammad, Yasser F. O. and Thovutikul, Sutasinee and Lala, Divesh and Abe, Masakazu and Ookaki, Takashi},
title = {Synthetic Evidential Study as Primordial Soup of Conversation},
booktitle = {Databases in Networked Information Systems --- 10th
International Workshop (DNIS 2015)},
series = {Lecture Notes in Computer Science},
volume = {8999},
pages = {74--83},
publisher = {Springer},
address = {Aizu-Wakamatsu, Japan},
year = {2015},
doi = {10.1007/978-3-319-16313-0_6}
}
Robotics & HRI
Mohammad, Y., & Nishida, T. (2013). What Is Human-Like Motion in a Shadowing Task? IROS 2013 Workshop: Towards Social Humanoid Robots: How to Make
Interaction Human-Like?
@inproceedings{mohammad2013humanlike,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {What Is Human-Like Motion in a Shadowing Task?},
booktitle = {IROS 2013 Workshop: Towards Social Humanoid Robots: How to Make
Interaction Human-Like?},
address = {Tokyo, Japan},
year = {2013}
}
Robotics & HRI
Mohammad, Y., & Nishida, T. (2010). Unsupervised Learning of Interactive Behavior for HRI. RSS 2010 Workshop on Learning for Human-Robot Interaction
Modeling.
@inproceedings{mohammad2010unsupervisedhri,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Unsupervised Learning of Interactive Behavior for {HRI}},
booktitle = {RSS 2010 Workshop on Learning for Human-Robot Interaction
Modeling},
address = {Zaragoza, Spain},
year = {2010}
}
In this paper, we present our efforts toward building
interactive robots that can learn how to interact naturally with
human partners in different environments and contexts. The main
feature of our approach is that it relies completely on
unsupervised learning and time series analysis techniques that
allow the robot to build its own interaction protocol
representation from the bottom up. The final controller of the
robot learned this way is a hierarchy of either dynamical systems
or probabilistic networks with complexity that is automatically
adjusted to the interaction protocol to be learned. We report two
examples of applying this technique to learn an explicit
interaction protocol in a master-slave settings (guided
navigation) and an implicit protocol in a teammate settings (a
listener robot).
Robotics & HRI
Mohammad, Y., Okada, S., & Nishida, T. (2010). Autonomous Development of Gaze Control for Natural Human-Robot
Interaction. IUI 2010 Workshop on Eye Gaze in Intelligent Human-Machine
Interaction, 63–70.
@inproceedings{mohammad2010gazedevelopment,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser and Okada, Shogo and Nishida, Toyoaki},
title = {Autonomous Development of Gaze Control for Natural Human-Robot
Interaction},
booktitle = {IUI 2010 Workshop on Eye Gaze in Intelligent Human-Machine
Interaction},
pages = {63--70},
publisher = {ACM},
year = {2010}
}
Gaze behavior is one of the most important nonverbal behaviors
during human-human close encounters. For this reason, many
researchers in natural human-robot interaction focus on
developing robots that can achieve human-like gaze behavior. Many
approaches have been proposed to achieve this natural gaze
behavior based on accurate analysis of human behaviors during
natural interactions. One limitation of most available approaches
is that the behavior is hardwired to the robot and learning
techniques are used only, if ever, for adjusting the parameters
of the behavior. In this paper we propose and evaluate a
different approach in which the robot natural gaze behavior by
watching natural interactions between humans. The proposed
approach uses the L iEICA architecture developed by the authors
and is completely unsupervised which leads to grounded behavior.
We compare the resulting gaze controller with a state-of-the-art
gaze controller that achieved human-like behavior and show that
the proposed approach leads to a more natural gaze behavior based
on subjective evaluations of subjects.
Robotics & HRI
Mohammad, Y. (2008). Toward Learning Interactive Behavior. IEEE ICRA 2008 Workshop on New Vistas and Challenges In
Telerobotics (NEWHRI).
@inproceedings{mohammad2008towardlearning,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser},
title = {Toward Learning Interactive Behavior},
booktitle = {IEEE ICRA 2008 Workshop on New Vistas and Challenges in
Telerobotics (NEWHRI)},
address = {Pasadena, CA, USA},
year = {2008}
}
This document is divided into two parts. The first part details
the authors opinion on the three questions posed by the ICRA
NEWHRI 2008 workshop. The second part represents a brief
introduction to the research conducted by the author toward
realization of autonomous robots that can learn interactive
behavior through real world interactions with humans.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2008). Toward Agents that Can Learn Nonverbal Interactive Behavior. IAPR Workshop on Cognitive Information Processing (CIP 2008), 164–169.
@inproceedings{mohammad2008nonverbal,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Toward Agents that Can Learn Nonverbal Interactive Behavior},
booktitle = {IAPR Workshop on Cognitive Information Processing (CIP 2008)},
pages = {164--169},
address = {Santorini, Greece},
year = {2008}
}
Humans are social agents and the social dimension is an
important aspect of human cognition. One challenge facing the
realization of artifacts and artificial agents that posses
human-like cognition abilities is to implement human-like
interactive capabilities into them. Natural Language Processing
is one of the earliest applications of AI techniques because of
the importance of language in shaping human cognitive and
interactive capabilities. Nevertheless nonverbal communication is
starting to gain more importance specially in the domains of HRI
and ECA because natural human-human communications are known to
utilize a variety of nonverbal interaction protocols. This paper
proposes a new adaptation algorithm for interactive agents that
aims to develop agents that can learn and adapt their theory of
mind concerning nonverbal interaction in real-time during actual
interactions. The proposed method utilizes elements of the theory
of theory and the theory of simulation to guide the adaptation
process. A proof of concept simulation experiment with the
proposed system is also illustrated.
Robotics & HRI
Mohammad, Y., & Nishida, T. (2008). Natural Listening Robot for AAL Applications. First International Workshop on IUI for Ambient Assisted Living
(IUI4AAL 2008).
@inproceedings{mohammad2008aal,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Natural Listening Robot for {AAL} Applications},
booktitle = {First International Workshop on IUI for Ambient Assisted Living
(IUI4AAL 2008)},
address = {Canary Islands, Spain},
year = {2008}
}
The increase in the proportion of senior citizens to the total
population is both a challenge and an opportunity for industrial
societies. To meet this challenge an increasing research effort
is being allocated to the area of Ambient Assisted Living (AAL).
Most of the research in this area is concerned with designing
novel interfaces and novel services based on embedded
intelligence targeting the elderly, and architectures to
integrate heterogeneous sensors and solutions. In this paper we
argue that a mobile robot companion can provide benefits to this
program that are beyond what is possible using only embedded
intelligence techniques. We further argue for the benefits of
utilizing the robots as knowledge media paradigm for AAL systems.
This paper then describes an ongoing effort to realize a humanoid
robot that can use human like nonverbal behavior to give the
interacting human a listening experiment as a first step for
realizing a useful knowledge media companion robot for AAL
applications. The details of a proof of applicability study of
our approach is given and discussed in relation to AAL.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2007). A New, HRI Inspired, View of Intention and Intention
Communication. AAAI-07 Workshop on Human Implications of Human-Robot
Interaction, 21–27.
@inproceedings{mohammad2007hriintention,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {A New, {HRI} Inspired, View of Intention and Intention
Communication},
booktitle = {AAAI-07 Workshop on Human Implications of Human-Robot
Interaction},
pages = {21--27},
address = {Vancouver, Canada},
year = {2007}
}
Intention is one of the most important concepts in understanding
human subjective existence as well as in creating naturally
interacting artifacts and robots. Currently intention is modeled
as a fixed yet may be unknown value that affects the behavior of
humans and is communicated in natural interactions. This folk
psychology inspired vision is underlying the understanding of
intention in AI, and HRI. Despite this wide utilization, this
view of intention is challenged by recent results in experimental
psychology and neuroscience. The contribution of this paper is
two fold: 1) A new theory for understanding both intention ( )
and intention communication ( ) is proposed and compared with the
traditional view. 2) Three concrete systems based on the proposed
view of intention in the areas of HRI, Intelligent User
Interfaces, and Robotic Control Architectures are introduced with
comparisons to traditional view based systems. This novelle
approach to intention can inform research in HRI toward more
naturally interactive robots and psychology toward a better
understanding of our own intentions.
Robotics & HRI
Mohammad, Y. F. O., & Nishida, T. (2007). EICA: Combining Interactivity with Autonomy for Social Robots. International Workshop on Social Intelligence Design (SID 2007)
, 227–236.
@inproceedings{mohammad2007eica,
keywords = {workshop},
focus = {robotics},
author = {Mohammad, Yasser F. O. and Nishida, Toyoaki},
title = {{EICA}: Combining Interactivity with Autonomy for Social Robots},
booktitle = {International Workshop on Social Intelligence Design (SID 2007)
},
pages = {227--236},
address = {Trento, Italy},
year = {2007}
}
The success of social robots in achieving natural coexistence
with humans depends on both their level of autonomy and their
interactive abilities. Although many robotic architectures have
been suggested and many researchers have focused on human-robot
interaction, a robotic architecture that can effectively combine
interactivity and autonomy is still unavailable. In this paper a
robotic architecture called EICA (Embedded Interactive Control
Architecture) is presented that tries to fill this gap. The
proposed architecture can help robotic designers in creating
sociable robots that can combine both natural interactivity with
humans and reactive fast response to the physical and social
environmental changes. The resulting robots can then be more
socially intelligent than the robots designed using current
deliberative, reactive or even hybrid architectures. [Keywords:]
Embodiment, EICA, Social Robotics, HRI
Mohammad, Y., & Nishida, T. (2006). Interactive Perception for Amplification of Intended Behavior in
Complex Noisy Environment. International Workshop on Social Intelligence Design (SID 2006)
, 173–187.
@inproceedings{mohammad2006interactiveperception,
keywords = {workshop},
author = {Mohammad, Yasser and Nishida, Toyoaki},
title = {Interactive Perception for Amplification of Intended Behavior in
Complex Noisy Environment},
booktitle = {International Workshop on Social Intelligence Design (SID 2006)
},
pages = {173--187},
address = {Osaka, Japan},
year = {2006}
}
Local Journals
ML & Time-Series
Ashraf, Z. M., Mohamed, Y. F., & Elsemman, I. E. (2022). Gemminer: Text Mining Tool for Genome-Scale Metabolic Model. Assiut University Journal of Multidisciplinary Scientific
Research, 51(3), 358–373.
@article{ashraf2022gemminer,
keywords = {localjournal},
focus = {timeseries},
author = {Ashraf, Zaynab M. and Mohamed, Yasser F. and Elsemman, Ibrahim E.},
title = {Gemminer: Text Mining Tool for Genome-Scale Metabolic Model},
journal = {Assiut University Journal of Multidisciplinary Scientific
Research},
volume = {51},
number = {3},
pages = {358--373},
year = {2022}
}
ML & Time-Series
Gad-Elrab, A. A. S., Mohammad, Y. F. O., & El-Melegy, M. T. (2020). Face Recognition from Small Datasets Using Kernel Selection of
Gabor Features. Journal of Engineering Sciences, Assiut University, 48(6), 1051–1071.
@article{gadelrab2020face,
keywords = {localjournal},
focus = {timeseries},
author = {Gad-Elrab, Alyaa A. S. and Mohammad, Yasser F. O. and El-Melegy, Moumen T.},
title = {Face Recognition from Small Datasets Using Kernel Selection of
Gabor Features},
journal = {Journal of Engineering Sciences, Assiut University},
volume = {48},
number = {6},
pages = {1051--1071},
year = {2020},
month = nov
}
Local Conferences
Others
Multiagent SystemsData Spaces
Achatz, R., Arosa Otero, D., Artman, A., Castellvi, S., Dobashi, M., Dong, L., Dreher, F., Fadrany, R. graves, Gerd-Gemein, O., Inanmis, H., Kivimäki, P., Koen, P., Laatikainen, G., Lähteenoja, V., López Aguilar, F., Mertens, C., Meyer, A. P., Mohammad, Y., Nagel, L., … Zangari, G. (2026). Data Spaces and AI: Trustworthy Agentic Participation in Data
Spaces (A. Turkmayali, Ed.) [IDSA Position Paper]. International Data Spaces Association (IDSA). https://doi.org/10.5281/zenodo.21279055
@techreport{idsa2026dataspacesai,
author = {Achatz, Reinhold and Arosa Otero, Daniel and Artman, Arian and Castellvi, Silvia and Dobashi, Masaru and Dong, Liu and Dreher, Felix and Fadrany, Roland graves and Gerd-Gemein, Olaf and Inanmis, Haluk and Kivim{\"{a}}ki, Petteri and Koen, Peter and Laatikainen, Gabriella and L{\"{a}}hteenoja, Viivi and L{\'{o}}pez Aguilar, Fernando and Mertens, Christoph and Meyer, Achim Pascal and Mohammad, Yasser and Nagel, Lars and Nemat, Andr{\'{e}} and Sakaino, Akira and Schlueter Langdon, Christoph and Tsiakas, Kosmas and Giakoumis, Dimitrios and Turpeinen, Marko and Ketterl, Markus and Virkkula, Sohvi and Zangari, Giuseppe},
editor = {Turkmayali, Anil},
title = {Data Spaces and AI: Trustworthy Agentic Participation in Data
Spaces},
institution = {International Data Spaces Association (IDSA)},
address = {Dortmund, Germany},
type = {IDSA Position Paper},
keywords = {other},
year = {2026},
focus = {multiagent,dataspace},
month = jul,
doi = {10.5281/zenodo.21279055},
url = {https://doi.org/10.5281/zenodo.21279055}
}
ML & Time-Series
Heracleous, P., Mohammad, Y., & Yoneyama, A. (2018). Feature Selection Using Deep Neural Networks for Speech Emotion
Recognition. Acoustical Society of Japan Autumn Meeting.
@inproceedings{heracleous2018featureselection,
keywords = {other},
focus = {timeseries},
author = {Heracleous, Panikos and Mohammad, Yasser and Yoneyama, Akio},
title = {Feature Selection Using Deep Neural Networks for Speech Emotion
Recognition},
booktitle = {Acoustical Society of Japan Autumn Meeting},
address = {Oita, Japan},
year = {2018}
}
Robotics & HRI
Mohammad, Y. (2008). Mutual Intention in Human-Robot Teams. Proceedings of the First Egyptian Japanese International
Symposium on Science and Technology (EJISST 2008), 122.
@inproceedings{mohammad2008mutualintention,
keywords = {other},
focus = {robotics},
author = {Mohammad, Yasser},
title = {Mutual Intention in Human-Robot Teams},
booktitle = {Proceedings of the First Egyptian Japanese International
Symposium on Science and Technology (EJISST 2008)},
pages = {122},
address = {Tokyo, Japan},
year = {2008}
}