Generative Adversarial Positive-Unlabeled Learning.
Ming Hou, Brahim Chaib-draa. Proc. of Int. Joint Conference on AI (IJCAI'18), (pdf), 2018.
2017
An Online Bayesian Filtering Framework for Gaussian Process.Application to Global Surface Temperature Analysis.
Wang Yali and Brahim Chaib-draa. Experts systems with Applications 67, 2017 (pdf).
Bayesian inference for time-varying applications : particle-based Gaussian process approaches.
Wang Yali and Brahim Chaib-draa. Neurocomputing (238), 2017 (pdf).
Parametric exponential linear unit for deep convolutional neural networks.
Ludovic Trottier, Philippe
Giguère and Brahim Chaib-draa. In Proc. of IEEE Int. Conf. on Machine Learning and Applications (ICMLA'17), (pdf).
Convolutional residual network for grasp localization.
Ludovic Trottier, Philippe
Giguère and Brahim Chaib-draa. In Proc. of 14th Conference on Computer and Robot Vision (CRV'17), (pdf).
Sparse dictionary learning for identifying grasp locations.
Ludovic Trottier, Philippe
Giguère and Brahim Chaib-draa. In Workshop on Applications of
Computer Vision (WACV'17), 2017 (pdf).
Deep object ranking for template matching.
Jean-Philippe Mercier, Ludovic Trottier, Philippe
Giguère and Brahim Chaib-draa. Workshop on Applications of
Computer Vision (WACV'17), 2017 (pdf).
Fast recursive low-rank tensor learning for regression.
Ming Hou, Brahim Chaib-draa. Proc. of Int. Joint Conference on AI (IJCAI'17), (pdf), 2017.
2016
Building Dialogue POMDPs from Expert Dialogues an End-to-End Approach.
Hamid Chinaei, Brahim Chaib-draa, Springer. Briefs in Electrical and Computer Engineering. Speech Technology. 2016
KNN-based Kalman Filter: an Efficient and Non-stationary Method for Gaussian Process Regression.
Wang Yali and Brahim Chaib-draa. Knowledge-based Systems 114, 2016 (pdf).
Common and Discriminative Subspace Kernel-based Multiblock Tensor Partial Least Squares Regression.
Ming Hou, Qibin Zhao, Brahim Chaib-draa and Andrzej Cichocki. In Proceedings of the Association for the
Advancement of Artificial Intelligence (AAAI'16), 2016 (pdf).
Sequential Inference for Deep Gaussian Process.
Wang Yali, Marcus
Brubaker, Brahim Chaib-draa and Raquel Urtasun. In Proceedings of the
19th International Conference on Artificial Intelligence and Statistics
(AISTATS'16), 2016. (pdf).
Online Incremental higher-order Partial Least Squares Regression for
Fast Reconstruction of Motion Trajectories from Tensor Streams.
Ming Hou and Brahim Chaib-draa, In 41th IEEE
Int. Conference on Acoustics, Speech and Signal Processing
(ICASSP'16), 2016 (pdf).
2015
Computing Equilibria in Discounted Dynamic Games
Andriy Burkov and Brahim Chaib-draa. In Applied Mathematics and Computation, 2015, (pdf).
Feature Selection for Robust Automatic Speech Recognition: A Temporal Offset Approach
Ludovic Trottier, Philippe Giguère and Brahim Chaib-draa. In International
Journal of Speech Technology, 2015, (pdf).
Learning Terrain Types with the Pitman-Yor Process Mixtures of Gaussians
for a Legged Robot.
Krzysztof Walas, Patrick Dallaire, Philippe Giguère and Brahim Chaib-draa. In Proc. of International Conference on Intelligent Robots and Systems (IROS'15), 2015, (pdf).
Incrementally Built Dictionary Learning for Sparse Representation
Ludovic Trottier, Brahim Chaib-draa and Philippe Giguère. In Proc. of International Conference on Neural Information Systems (ICONIP 15), 2015. (pdf).
Hierarchical Tucker Tensor Regression: Application to Brain Imaging Data Analysis.
Ming Hou and Brahim Chaib-draa. In IEEE Int. Conference on Image Processing (ICIP'15), Quebec, Canada, 2015. (pdf).
Temporal Feature Selection for Noisy Speech Recognition.
Ludovic Trottier, Brahim Chaib-draa and Philippe Giguère. In Proceedings of Canadian Artificial Intelligence, 2015, (pdf).
Online Local Gaussian Process for
Tensor-Variate Regression: Application to Fast
Reconstruction of Limb Movement from Brain Signal.
Ming Hou, Yali Wang and Brahim Chaib-draa, In 40th IEEE
Int. Conference on Acoustics, Speech and Signal Processing
(ICASSP'15), 2015, (pdf).
2014
Autonomous
Tactile Perception : A Combined Improved Sensing and
Bayesian Nonparametric Approach .
Patrick Dallaire, Philippe Giguère, Daniel Émond and Brahim
Chaib-draa, In Robotics and Autonomous Systems,
2014, (pdf).
Dialogue
POMDP components (part I): learning states and
observations.
Hamid Chinaei and Brahim Chaib-draa, In International
Journal of Speech Technology, 2014, (pdf).
Dialogue
POMDP components (Part II): learning the reward function.
Hamid Chinaei and Brahim Chaib-draa, In International
Journal of Speech Technology, 2014, (pdf).
Gaussian
Processes for Bayesian Estimation in Ordinary
Differential Equations.
Yali Wang, David Barber and , In International
Conference on Machine Learning (ICML) , 2014, (pdf).
Bayesian
Filtering with Online Gaussian Process Latent Variable
Models.
Yali Wang, Marcus Brubaker, Brahim Chaib-draa, Raquel
Urtasun and , In Uncertainty in Artificial
Intelligence (UAI), 2014, (pdf).
Dialogue
Strategy Learning in Healthcare: A Systematic Approach
for Learning Dialogue Models from Data.
Hamid Chinaei and Brahim Chaib-draa, In Proceedings
5th Workshop on Speech and Language Processing for
Assistive Technologies, (ACL'14), 2014, (pdf).
Effects of
Frequency-Based Inter-frame Dependencies on Automatic
Speech Recognition.
Ludovic Trottier, Brahim Chaib-draa and Philippe Giguère,
In Proceedings of Canadian Artificial Intelligence,
2014, (pdf).
Learning the
Structure of Probabilistic Graphical Models with an
Extended CIBP.
Patrick Dallaire, Philippe Giguère, Brahim Chaib-draa and
, In Proceedings of the Association for the
Advancement of Artificial Intelligence (AAA'14), 2014, (pdf).
2013
Repeated
Games for Multiagent Systems: A Survey.
Andriy Burkov and Brahim Chaib-draa, In The Knowledge
Engineering Review, 2013, (pdf).
Apprenticeship
Learning with few Examples .
Abdeslam Boularias and Brahim Chaib-draa, In Neurocomputing,
2013, (pdf).
A KNN Based
Kalman Filter Gaussian Process Regression.
Yali Wang, Brahim Chaib-draa and , In International
Joint Conference on Artificial Intelligence (IJCAI),
2013, (pdf).
2012
Stochastic
Resource Allocation in Multiagent Environments: an
Approach based on Distributed Q-Values and Bounded
Real-Time Dynamic Programming.
Pierrick Plamondon and Brahim Chaib-draa, In Int.
Journal of Artificial Intelligence Tools, 2012, (pdf).
Building
Adapative Dialogue Systems via Bayes-Adaptive POMDPs.
Shaowei Png, Joelle Pineau and Brahim Chaib-draa, In IEEE
Jour. of Selected Topics in Signal Processing, 2012,
(pdf).
An Adaptive
Nonparametric Particle Filter for State Estimation.
Yali Wang, Brahim Chaib-draa and , In IEEE
International Conference on Robotics and Automation
(ICRA), 2012, (pdf).
Learning
Observation Models for Dialogue POMDPs.
Hamid R. Chinaei, Brahim Chaib-draa and Luc Lamontagne, In
25th Canadian Conference on Artificial Intelligence
(AI'2012), 2012, (pdf).
A
Marginalized Particle Gaussian Process Regression.
Yali Wang, Brahim Chaib-draa and , In Neural
Information Processing Systems (NIPS'2012), 2012, (pdf).
An Inverse
Reinforcement Learning Algorithm for Partially
Observable Domains with Application on Healthcare
Dialogue Management..
Hamid R. Chinaei, Brahim Chaib-draa and , In 11th
International Conference on Machine Learning and
Applications (ICMLA'2012), 2012, (pdf).
2011
Articles in Referred Journals
An
approximate inference with Gaussian process to latent
functions from uncertain data.
Patrick Dallaire, Camille Besse, Brahim Chaib-draa and ,
In Neurocomputing, 2011, (pdf).
A Bayesian
Approach for Learning and Planning in Partially
Observable Markov Decision Processes.
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa and Pierre
Kreitmann, In Journal of Machine Learning Research,
1655--1696, 2011, (pdf).
Cooperative
Adaptative Cruise Control: A Reinforcement Learning
Approach.
Desjardins, Charles and Chaib-draa, Brahim and , In IEEE
Transactions on Intelligent Transportation Systems,
2011, (pdf).
Articles in Referred
Proceedings
Toward
Error-bounded Algorithms for Infinite-Horizon DEC-POMDPs.
Jilles S. Dibangoye, Abdel-Illah Mouaddib and Brahim
Chaib-draa, In Proceedings of 10th International
Conference on AAMAS, 2011, (pdf).
Learning
Dialogue POMDP Models from Data.
Hamid R. Chinaei, Brahim Chaib-draa and , In 24th
Canadian Conference on Artificial Intelligence (AI'2011),
2011, (pdf).
Tactile
Perception for Surface Identification Using a Triple
Axis Accelerometer Probe.
Dallaire, Patrick, Edmond, Daniel, Giguère, Philippe and
Chaib-draa, Brahim, In International Symposium on
Robotics and Sensors Environments (ROSE), Montréal,
Canada, 2011, (pdf).
2010
Articles in Referred Journals
Task
allocation learning in a multiagent environment:
Application to the RoboCupRescue.
Sébastien Paquet, Brahim Chaib-draa, Patrick Dallaire,
Danny Bergeron and , In Multiagent and Grid Systems,
2010, (pdf).
Book Chapters
Stochastic
Games.
Andriy Burkov and Brahim Chaib-draa, In Markov
Decision Processes and Artificial Intelligence,
Wiley - ISTE, 2010 (bib).
Articles in Referred
Proceedings
Apprenticeship
Learning via Soft Local Homomorphisms.
Abdeslam Boularias and Brahim Chaib-draa, In Proceedings
of 2010 IEEE International Conference on Robotics and
Automation (ICRA'10), Anchorage, USA, 2010, (pdf) .
Solving the
Continuous Time Multiagent Patrol Problem.
Jean-Samuel Marier, Camille Besse and Brahim Chaib-draa,
In Proceedings of 2010 IEEE International Conference
on Robotics and Automation (ICRA'10), 2010, (pdf).
Quasi-Deterministic
POMDPs and Dec-POMDPs.
Camille Besse, Brahim Chaib-draa and , In Proceedings
of 5th International Workshop On Multiagent Sequential
Decision Making in Uncertain Domains, 2010, (pdf). A shorter
version also appeared in Proceedings of 9th
International Conference On Autonomous Agents and
MultiAgent Systems (Extended Abstract), 2010, (pdf).
In this paper we study a particular
subclass of partially observable models called
quasi-deterministic partially observable Markov decision
processes (QDetPOMDPs) characterized by deterministic
transitions and stochastic observations. While this
framework does not model the same general problems as
POMDPs they still capture a number of interesting and
challenging problems and have in some cases interesting
properties. By studying the observability available in
this subclass we suggest that QDetPOMDPs may fall many
steps in the complexity hierarchy. An extension of this
framework to the decentralized case also reveals a
subclass of numerous problems that can be approximated in
polynomial space. Finally a sketch of e-optimal algorithms
for these classes of problems is given and empirically
evaluated.
An Approximate Subgame-Perfect Equilibrium
Computation Technique for Repeated Games.
Andriy Burkov,
Brahim Chaib-draa and , In Proceedings of
Twenty-Fourth Conference on Artificial Intelligence
(AAAI'10), 2010, (pdf).
Bootstrapping
Apprenticeship Learning.
Abdeslam Boularias, Brahim Chaib-draa and , In Advances
in Neural Information Processing Systems 24 (NIPS'10),
2010, (pdf).
Learning the
Reward Model of Dialogue POMDPs from Data.
Abdeslam Boularias, Hamid R. Chinaei, Brahim Chaib-draa
and , In NIPS 2010 workshop of Machine Learning for
Assistive Techniques, 2010, (pdf).
2009
Articles in Referred Journals
Effective
Learning in the Presence of Adaptive Counterparts.
Andriy Burkov, Brahim Chaib-draa and , In Journal of
Algorithms, 127--138, 2009, (pdf)
.
Book Chapters
Learning
Agents for Collaborative Driving.
Charles Desjardins, Julien Laumonier and Brahim
Chaib-draa, In Multi-Agent Systems for Traffic and
Transportation Engineering, IGI Global, 240--260,
2009, (pdf)
Articles in Referred
Proceedings
Policy
Iteration Algorithms for DEC-POMDPs with Discounted
Rewards.
Jilles S. Dibangoye, Brahim Chaib-draa, Abdel-Illah
Mouaddib and , In Proceedings of the Workshop on
Multi-Agent Sequential Decision Making in Uncertain
Domains (MSDM'09), Budapest, Hungary, 2009, (pdf).
Point-based
Incremental Pruning Heuristic for Solving Finite-Horizon
DEC-POMDPs.
Jilles S. Dibangoye, Abdel-Illah Mouaddib, Brahim
Chaib-draa and , In Proceedings of The 8th
International AAMAS Conference, 2009, (pdf).
Learning User
Intentions in Spoken Dialogue Systems.
Hamid R. Chinaei and B. Chaib-draa, In Proceedings of
1st International Conference on Agents and Artificial
Intelligence (ICAART'09) - Best student paper Award - ,
2009, (pdf)
.
Topological
Order Planner for POMDPs.
Jilles S. Dibangoye, Guy Shani, Brahim Chaib-draa,
Abdel-Illah Mouaddib and , In International Joint
Conference of Artificial Intelligence (IJCAI), 2009,
(pdf).
Predictive
Representations for Policy Gradient in POMDPs.
Abdeslam Boularias, Brahim Chaib-draa and , In Proceedings
of the Twenty-sixth International Conference on Machine
Learning (ICML'09), 2009, (pdf).
Multiagent
Learning and Optimality Criteria in Repeated Game
Self-play.
Andriy Burkov, Brahim Chaib-draa and , In Actes des
Cinquièmes Journées Francophones Modèles formels de
l’interaction, 93--100, 2009, (pdf).
Bayesian
Reinforcement Learning in POMDPs with Gaussian Processes.
P. Dallaire, C. Besse, S. Ross and B. Chaib-draa, In Proceedings
of the International Conference on Intellegent Robots
and Systems (IROS), , 2009, (pdf).
Anytime
Self-play Learning to Satisfy Functional Optimality
Criteria.
Andriy Burkov, Brahim Chaib-draa and , In Proceedings
of 1st International Conference On Algorithmic Decision
Theory (ADT'09), 446--457, 2009, (pdf).
Quasi-Deterministic
Partially Observable Markov Decision Processes.
Camille Besse and Brahim Chaib-draa, In Proceedings of
16th International Conference On Neural Information
Processing, 237--246, 2009, (pdf).
A Markov
Model for Multiagent Patrolling in Continuous Time.
Jean-Samuel Marier, Camille Besse, Brahim Chaib-draa and ,
In Proceedings of 16th International Conference On
Neural Information Processing, 648--656, 2009, (pdf).
Learning
Gaussian Process Models from Uncertain Data.
Patrick Dallaire, Camille Besse and Brahim Chaib-draa, In
Proceedings of 16th International Conference On Neural
Information Processing, 433--440, 2009, (pdf).
2008
Articles in Referred Journals
Online
Planning Algorithms for POMDPs.
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa, Sébastien
Paquet and , In Journal of AI Reserach (JAIR),
663--704, 2008, (pdf).
Spreadsheet
vs Multiagent Based Simulations: The Case of Supply
Chains.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and
Philippe Babin, In International Journal of simulation
and Process Modeling (IJPSM), 2008, (pdf).
Book Chapters
Une
introduction aux jeux stochastiques.
Andriy Burkov and Brahim Chaib-draa, In Processus
décisionnels de Markov en intelligence artificielle,
Hermès Science - Lavoisier, 135--178, 2008, (pdf).
Articles in Referred
Proceedings
Bayesian
Reinforcement Learning in Continuous POMDPs with
Application to Robot Navigation.
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa and , In Proceedings
of the 2008 IEEE International Conference on Robotics
and Automation (ICRA'08), 2008, (pdf).
Approximation
de politiques par renforcement et classification.
Julien Laumonier, Brahim Chaib-draa and , In 16e
congrès francophone AFRIF-AFIA, Reconnaissance des
Formes et Intelligence Artificielle - Récompensé comme
meilleur papier -, 2008, (pdf).
Prix de la meilleure contribution IA.
Parallel
Rollout for Online Solution of Dec-POMDP.
Camille Besse and Brahim Chaib-draa, In Proceedings of
21st International FLAIRS Conference, 619--624,
2008, (pdf).
State Space
Compression with Predictive Representations.
A. Boularias, M. Izadi, B. Chaib-draa and , In Proceedings
of the Twenty-First International Florida Artificial
Intelligence Research Society Conference (FLAIRS'08),
41--46, 2008, (pdf).
Exact Dynamic
Programming for Decentralized POMDPs with Lossless
Policy Compression.
A. Boularias, B. Chaib-draa and , In Proceedings of
the Eighteenth International Conference on Automated
Planning and Scheduling (ICAPS'08), 2008, (pdf).
High dimensionality of belief space
in DEC-POMDPs is one of the major causes that makes the
optimal joint policy computation intractable. The belief
state for a given agent is a probability distribution over
the system states and the policies of other agents. Belief
compression is an efficient POMDP approach that speeds up
planning algorithms by projecting the belief state space
to a low-dimensional one. In this paper, we introduce a
new method for solving DEC-POMDP problems, based on the
compression of the policy belief space. The reduced policy
space contains sequences of actions and observations that
are linearly independent. We tested our approach on two
benchmark problems, and the preliminary results confirm
that Dynamic Programming algorithm scales up better when
the policy belief is compressed.
Planning in
Decentralized POMDPs with Predictive Policy
Representations.
A. Boularias, B. Chaib-draa and , In Proceedings of
ICAPS'08 Multiagent Planning Workshop (MASPLAN'08),
2008, (pdf).
We discuss the problem of policy
representation in stochastic and partially observable
systems, and address the case where the policy is a hidden
parameter of the planning problem. We propose an
adaptation of the Predictive State Representations (PSRs)
to this problem by introducing tests (sequences of actions
and observations) on policies. The new model, called the
Predictive Policy Representations (PPRs), is more compact
and uses less parameters than the usual representations,
such as decision trees or Finite-State Controllers (FSCs).
In this paper, we show how PPRs can be used to improve the
performances of a point-based algorithm for DEC-POMDP.
Distributed
Planning in Stochastic Games with Communication.
Andriy Burkov and Brahim Chaib-draa, In Proceedings of
ICAPS'08 Multiagent Planning Workshop (MASPLAN'08),
2008, (pdf).
This paper treats the problem of
distributed planning in general-sum stochastic games with
communication when the model is known. Our main
contribution is a novel, game theoretic approach to the
problem of distributed equilibrium computation and
selection. We show theoretically and via experimentations
that our approach to multiagent planning, when adopted by
all agents, facilitates an efficient distributed
equilibrium computation and leads to a unique equilibrium
selection in general-sum stochastic games with
communication.
Prediction-directed
Compression of POMDPs.
Abdeslam Boularias, Masoumeh Izadi, Brahim Chaib-draa and
, In Proceedings of the International Conference on
Machine Learning and Applications (ICMLA'08, 2008, (pdf).
A Predictive
Model for Imitation Learning in Partially Observable
Environments.
Abdeslam Boularias and , In Proceedings of the
International Conference on Machine Learning and
Applications (ICMLA'08), 2008, (pdf).
Recherche
incrémentale à base de points pour la résolution des
DEC-POMDPs.
Jilles S. Dibangoye, Abdel-Illah Mouaddib, Brahim
Chaib-draa and , In Les Actes des 15es JFSMA,
2008, (pdf).
Planification
à base d'ordres topologiques pour la résolution des
POMDPs.
Jilles S. Dibangoye, Brahim Chaib-draa, Abdel-Illah
Mouaddib and , In Les Actes des 3es JFPDA, 2008, (pdf).
A Novel
Prioritization Technique for Solving Markov Decision
Processes.
Jilles S. Dibangoye, Brahim Chaib-draa, Abdel-Illah
Mouaddib and , In Proceedings of 21st International
FLAIRS Conference, 2008, (pdf).
2007
Articles in Referred Journals
Information
Sharing as a Coordination Mechanism for Reducing the
Bullwhip Effect in a Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In IEEE Transactions on Systems, Man, and
Cybernetics-Part C (SMC-C), 396--409, 2007, (pdf).
Multiagent
Coordination Techniques For Complex Environments: The
Case of a Fleet of Combat Ships.
Patrick Beaumont, Brahim Chaib-draa and , In IEEE
Transaction on Systems, Man and Cybernetics-Part C,
(SMC-C), 373--384, 2007, (pdf).
Conversational
Semantics with Social Commitments.
Roberto A. Flores, Philippe Pasquier, Brahim Chaib-draa
and , In Journal of Autonomous Agent and Multi-Agent
Systems, 165--186, 2007, (pdf).
Articles in Referred
Proceedings
Learning
to Play a Satisfaction Equilibria.
Stéphane Ross, Brahim Chaib-draa and , In
Evolutionary
Models of Collaboration (EM C'07) Workshop of Int. Joint
Conf. on AI (IJCAI'07), 2007, (
pdf).
AEMS:
An Anytime Online Search Algorithm for Approximate Policy
Refinement in Large POMDPs.
Stéphane Ross, Brahim Chaib-draa and , In
Proc. of Int.
Joint Conf. on AI (IJCAI'07), 2007, (
pdf).
Agent
Neighbourhood for Learning Approximated Policies in
DEC-MDP.
Julien Laumonier, Brahim Chaib-draa and , In
Evolutionary
Models of Collaboration (EMC'07) Workshop of Int. Joint
Conf. on AI (IJCAI'07), 2007, (
pdf).
Tight
Bounds for a Stochastic Resource Allocation Algorithm
Using Marginal Revenue.
Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak Benaskeur
and , In
Proceedings of the AAAI 2007 Spring Symposium
on Decision Theoretic and Game Theoretic Agents
(GTDT'2007), 2007, (
pdf).
This paper contributes to solve
effectively stochastic resource allocation problems known
to be NP-Complete. To address this complex resource
management problem, previous works on pruning the action
space of real-time heuristic search is extended. The
pruning is accomplished by using upper and lower bounds on
the value function. This way, if an action in a state has
its upper bound lower than the lower bound on the value of
this state, this action may be pruned in the set of
possible optimal actions for the state. This paper extends
this previous work by proposing tight bounds for problems
where tasks have to be accomplished using limited
resources. The marginal revenue bound proposed in this
paper compares favorably with another approach which
proposes bounds for pruning the action space.
A Real-time Dynamic Programming Decomposition Approach to
Resource Allocation.
Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak Benaskeur
and , In
Proceedings of the Information, Decision and
Control (IDC 2007), 2007, (
pdf).
per contributes to solve
effectively stochastic resource allocation problems known
to be NP-Complete. To address this complex resource
management problem, the merging of two approaches is made:
The Q-decomposition model, which coordinates reward
separated agents through an arbitrator, and the Labeled
Real-Time Dynamic Programming (LRTDP) approaches are
adapted in an effective way. The Qdecomposition permits to
reduce the set of states to consider, while LRTDP
concentrates the planning on significant states only. As
demonstrated by the experiments, combining these two
distinct approaches permits to further reduce the planning
time to obtain the optimal solution of a resource
allocation problem.
Online Policy Improvement in Large POMDPs via an Error
Minimization Search.
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa and , In
Proceedings
of the 2nd North East Student Colloquium on Artificial
Intelligence (NESCAI), 2007, (
pdf).
Partially Observable Markov
Decision Processes (POMDPs) provide a rich mathematical
framework for planning under uncertainty. However, most
real world systems are modelled by huge POMDPs that cannot
be solved due to their high complexity. To palliate to
this difficulty, we propose combining existing offline
approaches with an online search process, called AEMS,
that can improve locally an approximate policy computed
offline, by reducing its error and providing better
performance guarantees. We propose different heuristics to
guide this search process, and provide theoretical
guarantees on the convergence to ǫ-optimal solutions. Our
experimental results show that our approach can provide
better solution quality within a smaller overall time than
state-of-the-art algorithms and allow for interesting
online/offline computation tradeoff.
A Q-decomposition and Bounded RTDP Approach to Resource
Allocation.
Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak Benaskeur
and , In
Proceedings of the 2007 International
Conference on Autonomous Agents and Multiagent Systems
(AAMAS'07), 2007, (
pdf).
This paper contributes to solve
effectively stochastic resource allocation problems known
to be NP-Complete. To address this complex resource
management problem, a Qdecomposition approach is proposed
when the resources which are already shared among the
agents, but the actions made by an agent may influence the
reward obtained by at least another agent. The
Q-decomposition allows to coordinate these reward
separated agents and thus permits to reduce the set of
states and actions to consider. On the other hand, when
the resources are available to all agents, no
Qdecomposition is possible and we use heuristic search. In
particular, the bounded Real-time Dynamic Programming
(bounded rtdp) is used. Bounded rtdp concentrates the
planning on significant states only and prunes the action
space. The pruning is accomplished by proposing tight
upper and lower bounds on the value function.
Urban Traffic Control Based on Learning Agents.
Pierre-Luc Grégoire, Charles Desjardins, Julien Laumonier,
Brahim Chaib-draa and , In
Proceedings of the 10th
Internationnal IEEE Conference on Intelligent
Transportation Systems (ITSC'07), 2007, (
pdf).
The optimization of traffic light
control systems is at the heart of work in traffic
management. Many of the solutions considered to design
efficient traffic signal patterns rely on controllers that
use pre-timed stages. Such systems are unable to identify
dynamic changes in the local traffic flow and thus cannot
adapt to new traffic conditions. An alternative, novel
approach proposed by computer scientists in order to
design adaptive traffic light controllers relies on the
use of intelligents agents. The idea is to let autonomous
entities, named agents, learn an optimal behavior by
interacting directly in the system. By using machine
learning algorithms based on the attribution of rewards
according to the results of the actions selected by the
agents, we can obtain a control policy that tries to
optimize the urban traffic flow. In this paper, we will
explain how we designed an intelligent agent that learns a
traffic light control policy. We will also compare this
policy with results from an optimal pre-timed controller.
Architecture and Design of a Multi-Layered Cooperative
Cruise Control System.
Charles
Desjardins, Pierre-Luc Grégoire, Julien Laumonier and Brahim
Chaib-draa, In
Proceedings of the SAE World Congress,
2007 (
bib).
Bayes-Adaptive
POMDPs.
Stéphane Ross, Brahim Chaib-draa, Joelle Pineau and , In Proceedings
of the 21st conference on Neural Information Processing
Systems (NIPS'07), 2007, (pdf).esian Reinforcement Learning has
generated substantial interest recently, as it provides an
elegant solution to the exploration-exploitation trade-off
in reinforcement learning. However most investigations of
Bayesian reinforcement learning to date focus on the
standard Markov Decision Processes (MDPs). Our goal is to
extend these ideas to the more general Partially Observable
MDP (POMDP) framework, where the state is a hidden variable.
To address this problem, we introduce a new
mathematicalmodel, the Bayes-Adaptive POMDP. This new model
allows us to (1) improve knowledge of the POMDP domain
through interaction with the environment, and (2) plan
optimal sequences of actions which can tradeoff between
improving the model, identifying the state, and gathering
reward. We show how the model can be finitely
approximatedwhile preserving the value function. We describe
approximations for belief tracking and planning in this
model. Empirical results on two domains show that the model
estimate and agent?s return improve over time, as the agent
learns better model estimates.
Theoretical Analysis of Heuristic Search Methods for Online
POMDPs.
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa and , In Proceedings
of the 21st conference on Neural Information Processing
Systems (NIPS'07), 2007, (pdf).Planning in partially observable
environments remains a challenging problem, despite
significant recent advances in offline approximation
techniques. A few online methods have also been proposed
recently, and proven to be remarkably scalable, but without
the theoretical guarantees of their offline counterparts.
Thus it seems natural to try to unify offline and online
techniques, preserving the theoretical properties of the
former, and exploiting the scalability of the latter. In
this paper, we provide theoretical guarantees on an anytime
algorithm for POMDPs which aims to reduce the error made by
approximate offline value iteration algorithms through the
use of an efficient online searching procedure. The
algorithm uses search heuristics based on an error analysis
of lookahead search, to guide the online search towards
reachable beliefs with themost potential to reduce error. We
provide a general theorem showing that these search
heuristics are admissible, and lead to complete and
ǫ-optimal algorithms. This is, to the best of our knowledge,
the strongest theoretical result available for online POMDP
solution methods. We also provide empirical evidence showing
that our approach is also practical, and can find (provably)
near-optimal solutions in reasonable time.
A Markovian Model for Dynamic and Constrained Resource
Allocation Problems.
Camille Besse, Brahim Chaib-draa and , In Proceedings of
the 22nd AAAI Conference on Artificial Intelligence, July
2007, Vancouver, BC, Canada, 1846--1847, 2007, (pdf).An autonomous agent, allocating
stochastic resources to incoming tasks, faces increasingly
complex situations when formulating its control policy.
These situations are often constrained by limited resources
of the agent, time limits, physical constraints or other
agents. All these reasons explain why complexity and state
space dimension increase exponentially in size of considered
problem. Unfortunately, models that already exist either
consider the sequential aspect of the environment, or its
stochastic one or its constrained one. To the best of our
knowledge, there is no model that take into account all
these three aspects. For example, dynamic constraint
satisfaction problems (DCSP) have been introduced by Dechter
& Dechter (1988) to address dynamic and constrained
problems. However, in DCSPs, there is typically no
transition model, and thus no concept of sequence of
controls. On the other hand, Fargier, Lang, & Schiex
(1996) proposed mixed CSPs (MCSPs), but this approach
considers only the stochastic and the constrained aspects of
the problem. In this paper, we introduce a new model based
on DCSPs and Markov decision processes to address
constrained stochastic resource allocation (SRA) problems by
using expressiveness and powerfulness of CSPs. We thus
propose a framework which aims to model dynamic and
stochastic environments for constrained resources allocation
decisions and present some complexity and experimental
results.
An Efficient Model for Dynamic and Constrained Resource
Allocation Problems.
Camille Besse and Brahim Chaib-draa, In Proceedings of
the 2nd International Workshop on Constraint Satisfaction
Techniques for Planning and Scheduling Problems
(COPLAS'07), 9--16, 2007, (pdf).ynamic constraint satisfaction is a
useful tool for representing and solving sequential decision
problems with complete knowledge in dynamic world and
particularly constrained resource allocation problems.
However, when resources are unreliable, this framework
becomes limited due to the stochastic outcomes of the
assignments chosen. On the contrary, Markov Decision
Processes (MDPs) handle stochastic outcomes of unreliable
actions, but their complexity explodes when using
state-defined constraints. We thus propose an extension of
the MDP framework so as to represent constrained and
stochastic actions in sequential decision making. The basis
of this extension consists in modeling the evolution of a
dynamic constraint network by a MDP. We first study the
complexity of the problem of finding an optimal policy for
this model and then we propose an algorithm for solving it.
Comparison to standard MDP shows that this framework
noticeably improves policy computation.
R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a
Stochastic Resource Allocation Problem.
Camille Besse, Pierrick Plamondon and Brahim Chaib-draa, In
Proceedings of the 20th Canadian Conference on Artificial
Intelligence (AI'2007), 50--60, 2007, (pdf).Resource allocation is a widely
studied class of problems in Operation Research and
Artificial Intelligence. Specially, constrained stochastic
resource allocation problems, where the assignment of a
constrained resource do not automatically imply the
realization of the task. This kind of problems are generally
addressed with Markov Decision Processes (mdps). In this
paper, we present efficient lower and upper bounds in the
context of a constrained stochastic resource allocation
problem for a heuristic search algorithm called Focused Real
Time Dynamic Programming (frtdp). Experiments show that this
algorithm is relevant for this kind of problems and that the
proposed tight bounds reduce the number of backups to
perform comparatively to previous existing bounds.
Les Représentations Prédictives des états et des Politiques.
A. Boularias, B. Chaib-draa and , In Actes des
Quatrièmes Journées Francophones Modèles Formels de
l'Interaction (MFI'07), 37--48, 2007, (pdf).Nous proposons dans cet article une
nouvelle approche pour repr?senter les politiques
(strat?gies) dans les environnements stochastiques et
partiellement observables. Nous nous int?ressons plus
particuli?rement aux syst?mes multi-agents, o? chaque agent
conna?t uniquement ses propres politiques, et doit choisir
la meilleure parmi elles selon son ?tat de croyance sur les
politiques du reste des agents. Notre mod?le utilise moins
de param?tres que les m?thodes de repr?sentation usuelles,
telles que les arbres de d?cision ou les controleurs d'?tats
finis stochastiques, permettant ainsi une acc?l?ration des
algorithmes de planification. Nous montrons aussi comment ce
mod?le peut ^etre utilis? efficacement dans le cas de la
planification multiagents coop?rative et sans communication,
les r?sultats empiriques sont compar?s avec le mod?le
DEC-POMDP (Decentralized Partially Observable Markov
Decision Process).
Multiagent Learning in Adaptive Dynamic Systems.
Andriy Burkov and Brahim Chaib-draa, In Proceedings of
the 2007 International Conference on Autonomous Agents and
Multiagent Systems (AAMAS'07), 2007, (pdf).Classically, an approach to the
multiagent policy learning supposed that the agents, via
interactions and/or by using preliminary knowledge about the
reward functions of all players, would find an
interdependent solution called ``equilibrium''. Recently,
however, certain researchers question the necessity and the
validity of the concept of equilibrium as the most important
multiagent solution concept. They argue that a ``good''
learning algorithm is one that is efficient with respect to
a certain class of counterparts. Adaptive players is an
important class of agents that learn their policies
separately from the maintenance of the beliefs about their
counterparts' future actions and make their decisions based
on that policy and the current belief. In this paper, we
propose an efficient learning algorithm in presence of the
adaptive counterparts called Adaptive Dynamics Learner
(ADL), which is able to learn an efficient policy over the
opponents' adaptive dynamics rather than over the simple
actions and beliefs and, by so doing, to exploit these
dynamics to obtain a higher utility than any equilibrium
strategy can provide. We tested our algorithm on a
substantial representative set of the most known and
demonstrative matrix games and observed that ADL agent is
highly efficient against Adaptive Play Q-learning (APQ)
agent and Infinitesimal Gradient Ascent (IGA) agent. In
self-play, when possible, ADL is able to converge to a
Pareto optimal strategy maximizing the welfare of all
players.
Labeled Initialized Adaptive Play Q-learning for Stochastic
Games.
Andriy Burkov and Brahim Chaib-draa, In Proceedings of
the AAMAS'07 Workshop on Adaptive and Learning Agents
(ALAg'07), 2007, (pdf).Recently, initial approximation of
Q-values of the multiagent Q-learning by the optimal
single-agent Q-values has shown good results in reducing the
complexity of the learning process. In this paper, we
continue in the same vein and give a brief description of
the Initialized Adaptive Play Q-learning (IAPQ) algorithm
while establishing an effective stopping criterion for this
algorithm. To do that, we adapt a technique called
``labeling'' to the multiagent learning context. Our
approach demonstrates good empirical behavior in multiagent
coordination problems, such as two-robot grid world
stochastic game. We show that our Labeled IAPQ (i) is able
to converge faster than IAPQ by permitting a certain
predefined value of learning error and (ii) it establishes
an effective stopping criterion, which permits terminating
the learning process at a near-optimal point with a flexible
learning speed/quality tradeoff.
Competition and Coordination in Stochastic Games.
Andriy Burkov, Abdeslam Boularias and Brahim Chaib-draa, In
Proceedings of the 2007 Twentieth Canadian Conference on
Artificial Intelligence (CanAI'07), 2007, (pdf).Agent competition and coordination
are two classical and most important tasks in multiagent
systems. In recent years, there was a number of learning
algorithms proposed to resolve such type of problems. Among
them, there is an important class of algorithms, called
adaptive learning algorithms, that were shown to be able to
converge in self-play to a solution in a wide variety of the
repeated matrix games. Although certain algorithms of this
class, such as Infinitesimal Gradient Ascent (IGA), Policy
Hill-Climbing (PHC) and Adaptive Play Q-learning (APQ), have
been catholically studied in the recent literature, a
question of how these algorithms perform versus each other
in general form stochastic games is remaining
little-studied. In this work we are trying to answer this
question. To do that, we analyse these algorithms in detail
and give a comparative analysis of their behavior on a set
of competition and coordination stochastic games. Also, we
introduce a new multiagent learning algorithm, called
ModIGA. This is an extension of the IGA algorithm, which is
able to estimate the strategy of its opponents in the cases
when they do not explicitly play mixed strategies (e.g.,
APQ) and which can be applied to the games with more than
two actions.
Effective Learning in Adaptive Dynamic Systems.
Andriy Burkov and Brahim Chaib-draa, In Proceedings of
the AAAI 2007 Spring Symposium on Decision Theoretic and
Game Theoretic Agents (GTDT'07), 2007, (pdf).Classically, an approach to the
policy learning in multiagent systems supposed that the
agents, via interactions and/or by using preliminary
knowledge about the reward functions of all players, would
find an interdependent solution called ``equilibrium''.
Recently, however, certain researchers question the
necessity and the validity of the concept of equilibrium as
the most important multiagent solution concept. They argue
that a ``good'' learning algorithm is one that is efficient
with respect to a certain class of counterparts. Adaptive
players is an important class of agents that learn their
policies separately from the maintenance of the beliefs
about their counterparts' future actions and make their
decisions based on that policy and the current belief. In
this paper we propose an efficient learning algorithm in
presence of the adaptive counterparts called Adaptive
Dynamics Learner (ADL) which is able to learn an efficient
policy over the opponents' adaptive dynamics rather than
over the simple actions and beliefs and, by so doing, to
exploit this dynamics to obtain a higher utility than any
equilibrium strategy can provide. We tested our algorithm on
a big set of the most known and demonstrative matrix games
and observed that ADL agent is highly efficient against
Adaptive Play Q-learning (APQ) agent and Infinitesimal
Gradient Ascent (IGA) agent. In self-play, when possible,
ADL is able to converge to a Pareto optimal strategy that
maximizes the welfare of all players instead of an
equilibrium strategy.
Adaptive Play Q-Learning with Initial Heuristic
Approximation.
Andriy Burkov and Brahim Chaib-draa, In Proceedings of
the 2007 IEEE International Conference on Robotics and
Automation (ICRA'07), 2007, (pdf).The problem of an effective
coordination of multiple autonomous robots is one of the
most important tasks of the modern robotics. In turn, it is
well known that the learning to coordinate multiple
autonomous agents in a multiagent system is one of the most
complex challenges of the state-of-the-art intelligent
system design. Principally, this is because of the
exponential growth of the environment's dimensionality with
the number of learning agents. This challenge is known as
``curse of dimensionality'', and relates to the fact that
the dimensionality of the multiagent coordination problem is
exponential in the number of learning agents, because each
state of the system is a joint state of all agents and each
action is a joint action composed of actions of each agent.
In this paper, we address this problem for the restricted
class of environments known as goal-directed stochastic
games with action-penalty representation. We use a
single-agent problem solution as a heuristic approximation
of the agents' initial preferences and, by so doing, we
restrict to a great extent the space of multiagent learning.
We show theoretically the correctness of such an
initialization, and the results of experiments in a
well-known two-robot grid world problem show that there is a
significant reduction of complexity of the learning process.
Periodic Real-Time Resource Allocation for Teams of
Progressive Processing Agents.
Jilles S. Dibangoye, Abdel-Illah Mouaddib, Brahim Chaib-draa
and , In
Proceedings of The 6th International Joint
Conference on Autonomous Agents and Multi-Agent Systems
(AAMAS'07), 2007, (
pdf).
2006
Articles in Referred Journals
DIAGAL : An agent communication
language based on dialogues games and sustained by social
commitments.
Marc-André Labrie, Mathieu Bergeron, Brahim Chaib-draa,
Philippe Pasquier and , In
Journal of Autonomous Agent
and Multi-Agent Systems, 61--95, 2006, (
pdf).
ecent years, social commitment
based approaches have been proposed to solve problems
issuing from previous mentalistic based semantics for
agent communication languages. This paper follows the same
line of thought since it presents the latest version of
our dialogue game based agent communication language ?
DIAlogue-Game based Agent Language (DIAGAL) ? which allows
agents to manipulate the public layer of social
commitments through dialogue, by creating, canceling and
updating their social commitments. To make apparent such
commitments, we consider here Agent Communication Language
(ACL) from the dialectic point of view, where agents ?play
a game? based on commitments. Such games based on
commitments are incorporated in the DIAGAL language, which
has been developed having in mind the following questions:
(a) What kind of structure does the game have? How are
rules specified within the game? (b) What kind of games
compositions are allowed? (c) How do participants in
conversations reach agreement on the current game? How are
games opened or closed? Using such games we show how we
can study the commitments dynamic to model agent dialogue
and we present metrics that can be used to evaluate the
quality of a dialogue between agents. Next, we use an
example (summer festival organization) to show how DIAGAL
can be used in analyzing and modeling automated
conversations in offices. Finally, we present the results
and analysis of the summer festival simulations that we
realized through our dialogue game simulator (DGS).
Prise de décision en temps-réel pour des POMDPs de
grande taille.
Sébastien Paquet, Ludovic Tobin, Brahim Chaib-draa and , In
Revue d'intelligence artificielle, 203--233, 2006, (pdf)
This paper
presents a POMDP approximation method, called RTBSS
(Real-Time Belief Space Search), which is based on a
look-ahead search in order to plan in a real-time dynamic
environment. The basis of our approach is to avoid computing
full policies in POMDP problems. Our approach is especially
motivated by real-time environments where the state space is
too large to consider traditional offline algorithms. We
then proceed with an online approach to find at each step,
the action that maximize the agent expected utility. To this
end, we present the formalism behind our approach. Then, we
present how the approach was applied on three different
environments: Tag, RockSample and the RoboCupRescue
simulation. Let us mention finally, that the approach we
present was successfully implemented for the RoboCupRescue
2004 international competition in Lisbon, Portugal where we
finished in second position.
Performance of software agents
in non-transferable payoff group buying.
Frederick Asselin, Brahim Chaib-draa and , In
Journal of
Experimental and Theoretical Artificial Intelligence,
1--32, 2006, (
pdf).
Apprentissage de la
coordination multiagent : une méthode basée sur le
Q-learning par jeu adaptatif.
Olivier Gies, Brahim Chaib-draa and , In
Revue
d'intelligence artificielle, 385--412, 2006, (
pdf).
Software agents can be useful in
forming buyers? groups since humans have considerable
difficulties in finding Pareto-optimal deals (no buyer can
be better without another being worse) in negotiation
situations. What are the computational and economical
performances of software agents for a group buying
problem? We have developed a negotiation protocol for
software agents which we have evaluated to see if the
problem is difficult on average and why. This protocol
probably finds a Pareto-optimal solution and, furthermore,
minimizes the worst distance to ideal among all software
agents given strict preference ordering. This evaluation
demonstrated that the performance of software agents in
this group buying problem is limited by memory
requirements (and not execution time complexity). We have
also investigated whether software agents following the
developed protocol have a different buying behaviour from
that which the customer they represented would have had in
the same situation. Results show that software agents have
a greater difference of behaviour (and better behaviour
since they can always simulate the obvious customer
behaviour of buying alone their preferred product) when
they have similar preferences over the space of available
products. We also discuss the type of behaviour changes
and their frequencies based on the situation.Current algorithmes on multiagent
learning are for almost limited since they cannot manage
the multiplicity of Nash equilibria and thus converge to
the Pareto-optimal. To alleviate this, we propose here a
learning mechanism extending the Q-learning to
non-cooperative stochastique games. This learning
mechanism converges to Pareto-optimal equilibria in
selfplay. We present experimental results showing
convergence of such learning mechanism. We then extend our
approach to the case of non-stationarity of agents which
is another important aspect of multiagent systems.
Finally, we tackle the question of non-stationarity in
multiagent environments in its generality and we present
in this context some research avenues which can lead to
improve our preliminary results on adaptation.
Book Chapters
Supply Chain Management and
Multiagent Systems: An Overview.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In MultiAgent-Based Supply Chain Management,
Springer, 2006, (pdf).This chapter introduces the topic
of this book by presenting the fields of supply chain
management, multiagent systems, and the merger of these
two fields into multiagent-based supply chain management.
More precisely, the problems encountered in supply chains
and the techniques to address these problems are first
presented. Multiagent systems are next broadly presented,
before focusing on how agents can contribute to solving
problems in supply chains.
Design, Implementation and
Test of Collaborative strategies in the Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In MultiAgent-Based Supply Chain Management,
Springer, approx 450 p., 2006, (pdf).In general, game theory is used to
analyze interactions formally described by an analytical
model. In this paper, we describe a methodology to replace
the analytical model by a simulation one in order to study
more realistic situations. We use this methodology to
study how the more-or-less selfishness of agents affects
their behaviour. We illustrate our methodology with the
case study of a wood supply chain, in which every company
is seen as an agent which may use an ordering strategy
designed to reduce a phenomenon called the bullwhip
effect. To this end, we assume that every agent utility
can be split in two parts, a first part representing the
direct utility of agents (in practice, their inventory
holding cost) and a second part representing agent social
consciousness, i.e., their impact on the rest of the
multi-agent system (in practice, their backorder cost). We
find that company-agents often apply their collaborative
strategy at whatever their same level of social
consciousness. Our interpretation of this specific case
study is that every company is so strongly related with
one other, that all should collaborate in our supply chain
model. Note that a previous paper outlined this
methodology and detailed its application to supply chains;
our focus is now on the presentation and the extension of
the methodology, rather than on its application to supply
chains.
Edited Books
Multiagent-based
Supply Chain Management.
Brahim Chaib-draa and Jörg Müller, Springer, 2006 (bib).
Articles in Referred
Proceedings
Cooperative Adaptive Cruise
Control: a Reinforcement Learning Approach.
Julien Laumonier, Charles Desjardins, Brahim Chaib-draa
and , In 4th Workshop on Agents in Traffic And
Transportation, AAMAS'06, 2006, (pdf).As a part of Intelligent Transport
Systems (ITS), Cooperative Adaptive Cruise Control (CACC)
systems have been introduced for finding solutions to the
modern problems of automotive transportation such as
traffic efficiency, passenger comfort and security. To
achieve cooperation, actors on the road must use internal
sensors and communication. Designing such a controller is
not an easy task when the problem is considered in its
entirety, since the interactions taking place in the
environment (from vehicle physics and dynamics to
multi-vehicle interaction) are extremely complex and hard
to model formally. That is why many ITS approaches
consider many levels of functionnalities. In this article,
we will show our work toward the design of a
multiple-level architecture using reinforcement learning
techniques. We explain our work on the design of a
longitudinal ACC controller, which is the first step
toward a fully functionnal CACC low-level controller. We
describe the design of our high-level controller used for
vehicles coordination. Preliminary results show that, in
some situations, the vehiclefollowing controller is
stable. We also show that the coordination controller
allows to have an efficient lane allocation for vehicles.
At last, we present some future improvements that will
integrate both approaches in a general architecture
centered on the design of a CACC system using
reinforcement learning.
Partial Local FriendQ
Multiagent Learning: Application to Team Automobile
Coordination Problem.
Julien Laumonier, Brahim Chaib-draa and , In Canadian
AI, 2006, (pdf).Real world multiagent coordination
problems are important issues for reinforcement learning
techniques. In general, these problems are partially
observable and this characteristic makes the solution
computation intractable. Most of the existing approaches
calculate exact or approximate solutions using the world
model for only one agent. To handle a special case of
partial observability, this article presents an approach
to approximate the policy measuring a degree of
observability for pure cooperative vehicle coordination
problem. We compare empirically the performance of the
learned policy for totally observable problems and
performances of policies for different degrees of
observability. If each degree of observability is
associated with communication costs, multiagent system
designers are able to choose a compromise between the
performance of the policy and the cost to obtain the
associated degree of observability of the problem.
Finally, we show how the available space, surrounding an
agent, influence the required degree of observability for
near-optimal solution.
Hybrid POMDP
Algorithms.
Sébastien Paquet, Brahim Chaib-draa, Stéphane Ross and ,
In Proceedings of The Workshop on Multi-Agent
Sequential Decision Making in Uncertain Domains (MSDM),
2006, (pdf).
Integrating
Social Commitment-Based Communication in Cognitive Agent
Modelling.
Philippe Pasquier, Roberto Flores, Brahim Chaib-draa and ,
In Proceedings of The International Workshop on agent
Communication (ACL'06), AAMAS' 06, 2006, (pdf)
Modelling the
Links Between Social Commitments and Individual
Intentions.
Philippe Pasquier, Roberto Flores, Brahim Chaib-draa and ,
In Proceedings of The 5th International Joint
Conference on Autonomous Agents and MultiAgent Systems
(AAMAS'06), 2006, (pdf).
An Ontology
of Social Control Tools.
Philippe Pasquier, Roberto Flores, Brahim Chaib-draa and ,
In Proceedings of The 5th International Joint
Conference on Autonomous Agents and MultiAgent Systems
(AAMAS'06), 2006, (pdf).
An Efficient
Resource Allocation Approach in Real-time Stochastic
Environment.
Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak
Benaskeur and , In Canadian AI, 2006, (pdf).
A Multiagent Task Associated
MDP (MTAMDP) Approach to Resource Allocation.
Pierrick Plamondon, Brahim
Chaib-draa, Abderrezak Benaskeur and , In AAAI 2006
Spring Symposium on Distributed Plan and Schedule
Management, 2006, (pdf).We are interested in contributing
to solving effectively the a specific type of real-time
stochastic resource allocation problem, which is known as
NP-Hard, of which the main distinction is the high number
of possible interacting actions to execute in a group of
tasks. To address this complex resource management
problem, we propose an adaptation of the Multiagent Markov
Decision Process (MMDP) model which centralizes the
computation of interacting resources. This adaptation is
called Multiagent Task Associated Markov Decision Process
(MTAMDP) and produces a near-optimal solution policy in a
much lower time than a standard MMDP approach. In a
MTAMDP, a planning agent computes a policy for each
resource, and are coordinated by a central agent. MTAMDPs
enables to practically solve our NP-Hard problem.
Satisfaction Equilibrium:
Achieving Cooperation in Incomplete Information Games.
Stéphane Ross, Brahim Chaib-draa and , In Canadian AI,
2006, (pdf).So far, most equilibrium concepts
in game theory require that the rewards and actions of the
other agents are known and/or observed by all agents.
However, in real life problems, agents are generally faced
with situations where they only have partial or no
knowledge about their environment and the other agents
evolving in it. In this context, all an agent can do is
reasoning about its own payos and consequently, cannot
rely on classical equilibria through deliberation, which
requires full knowledge and observability of the other
agents. To palliate to this diculty, we introduce the
satisfaction principle from which an equilibrium can arise
as the result of the agents' individual learning
experiences. We de ne such an equilibrium and then we
present dierent algorithms that can be used to reach it.
Finally, we present experimental results that show that
using learning strategies based on this speci c
equilibrium, agents will generally coordinate themselves
on a Pareto-optimal joint strategy, that is not always a
Nash equilibrium, even though each agent is individually
rational, in the sense that they try to maximize their own
satisfaction.
Study of
social consciousness in stochastic agent-based
simulations: Application to supply chains.
Philippe Pasquier, Roberto Flores, Brahim Chaib-draa and ,
In Proceedings of The 5th International Joint
Conference on Autonomous Agents and MultiAgent Systems
(AAMAS'06), 2006, (pdf).
Learning the
Required Number of Agents for Complex Tasks.
Sébastien Paquet, Brahim Chaib-draa and , In Proceedings
of The Fifth International Joint Conference on
Autonomous Agents & Multi Agent Systems (AAMAS-06),
2006, (pdf).
A Technique for Large
Automated Mechanism Design Problems.
Frederick Asselin, Brigitte Jaumard, Antoine Nongaillard
and , In Proceedings of the IEEE/WIC/ACM Conference on
Intelligent Agent Technology (IAT'06), 2006, (pdf).Automated mechanism design (AMD)
seeks to find, using algorithms, the optimal rules of
interaction (a mechanism) between selfish and rational
agents in order to get the best outcome. Here optimal is
defined by the objective function of the designer of the
mechanism where the function has usually some desirable
properties (e.g. Pareto optimal). A difficulty with AMD
lies in the size of the optimization problem that one
needs to solve in order to select the best mechanism:
there is a huge number of variables (and constraints but
to a lesser extent) even for AMD instances of relatively
small size. We study how to adapt the column generation
techniques in order to solve the linear programming LP
formulation of the AMD problem and compare its efficiency
with the classical simplex algorithm for linear programs,
on a bartering of goods example. We show that the
resulting column generation algorithm is very quickly
faster than the simplex algorithm for a fixed number of
types (i.e., preference relations) on the goods as the
number of goods increases, and then for a fixed number of
goods as the number of types increases. Moreover, we show
that, as the number of goods increases, the percentage of
variables that need to be explicitly considered by the
column generation techniques comes down very fast while
the simplex algorithm must always consider explicitly all
variables.
Resolution-based Policy
Search for Imperfect-information Differential Games.
Minh Nguyen-Duc, Brahim Chaïb-draa and , In Proceedings
of the IEEE/WIC/ACM Conference on Intelligent Agent
Technology (IAT'06), 2006, (pdf).Differential games (DGs),
considered as a typical model of game with continuous
states and non-linear dynamics, play an important role in
control and optimization. Finding optimal/approximate
solutions for these game in the imperfect information
setting is currently a challenge for mathematicians and
computer scientists. This article presents a multi-agent
learning approach to this problem. We hence propose a
method called resolution-based policy search, which uses a
limited non-uniform discretization of a perfect
information game version to parameterize policies to
learn. We then study the application of this method to an
imperfect information zero-sum pursuit-evasion game (PEG).
Experimental results demonstrate strong performance of our
method and show that it gives better solutions than those
given by traditional analytical methods.
A Q-decomposition LRTDP
Approach to Resource Allocation.
Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak
Benaskeur and , In Proceedings of the IEEE/WIC/ACM
International Conference on Intelligent Agent Technology
(IAT 2006), 2006, (pdf).his paper contributes to solve
effectively stochastic resource allocation problems known
to be NP-Complete. To address this complex resource
management problem, the merging of two approaches is made:
The Q-decomposition model, which coordinates reward
separated agents through an arbitrator, and the Labeled
Real-Time Dynamic Programming (LRTDP) approaches are
adapted in an effective way. The Q-decomposition permits
to reduce the set of states to consider, while LRTDP
concentrates the planning on significant states only. As
demonstrated by the experiments, combining these two
distinct approaches permits to further reduce the planning
time to obtain the optimal solution of a resource
allocation problem.
2005
Articles in Referred Journals
Agent
Communication Pragmatics: The Cognitive Coherence
Approach.
Philippe Pasquier, Brahim Chaib-draa and , In Journal
of Cognitive Systems Research, 364--395, 2005, (pdf).
A
Collaborative Driving System based on Multiagent
Modelling and Simulations.
Simon Hallé, Brahim Chaib-draa and , In Journal of
Transportation Research Part C (TRC-C): Emergent
Technologies, 320--345, 2005, (pdf).
Book Chapters
Collaborative
Driving System Using Teamwork for Platoon Formations.
Simon Hallé, Brahim Chaib-draa and , In Applications
of Agent Technology in Traffic and Transportation,
Birkhäuser, 2005, (pdf).
Articles in Referred
Proceedings
ACL:
Specification, Design and Analysis All Based on
Commitments.
Mathieu Bergeron, Brahim Chaib-draa and , In Proceedings
of the Workshop on Agent Communication (AC2005), fourth
International Joint Conference on Autonomous Agents and
Multi Agent , Systems (AAMAS 2005), 2005, (pdf).
Decomposition
Techniques for a Loosely-Coupled Resource Allocation
Problem.
P. Plamondon, B. Chaib-draa, A. Benaskeur and , In Proceedings
of the IEEE/WIC/ACM International Conference on
Intelligent Agent Technology (IAT 2005), 2005, (pdf).
A Layered
Model for Message Semantics using Social Commitments.
Roberto A. Flores, Philippe Pasquier and Brahim
Chaib-draa, In Proceedings of the 4th International
Joint Conference on Autonomous Agents and Multi Agent
Systems (AAMAS'2005), 1323--1324, 2005, poster.
Multiagent
Q-Learning: Preliminary Study on Dominance between the
Nash and Stackelberg Equilibriums.
Julien Laumonier, Brahim Chaib-draa and , In Proceedings
of AAAI-2005 Workshop on Multiagent Learning, 2005,
(pdf).
Modeling
Flexible Social Commitments and their Enforcement.
Philippe Pasquier, Roberto Flores, Brahim Chaib-draa and ,
In Proceedings of the Fifth International Workshop
Engineering Societies in the Agents World (ESAW),
M.-P. Gleizes and A. Omicini and F. Zambonelli, 153--165,
2005, (pdf).
DIAGAL: an ACL
ready for Open System.
Philippe Pasquier, Mathieu Bergeron, Brahim Chaib-draa and
, In Proceedings of the Fifth International Workshop
Engineering Societies in the Agents World (ESAW),
M.-P. Gleizes and A. Omicini and F. Zambonelli, 139--152,
2005, (pdf).
An Online
POMDP Algorithm for Complex Multiagent Environments.
Sébastien Paquet, Ludovic Tobin, Brahim Chaib-draa and ,
In Proceedings of The 4th International Joint
Conference on Autonomous Agents & Multi Agent
Systems (AAMAS'2005), 2005, (pdf).
Apprentissage
de la coordination multiagent: Q-learning par jeu
adaptatif.
Olivier Gies, Brahim Chaib-draa and , In Actes des
Troisièmes Journées Francophones Modèles Formels de
l'Interaction (MFI'2005), 2005, (pdf).
Coordination
d'agents à l'aide d'un algorithme en-ligne pour les
POMDPs.
Sébastien Paquet, Ludovic Tobin, Brahim Chaib-draa and ,
In Actes des Troisièmes Journées Francophones Modèles
Formels de l'Interacti on (MFI'2005), 2005.
Multiagent
Systems Viewed as Distributed Scheduling Systems:
Methodology and Experiments.
Sébastien Paquet, Nicolas Bernier, Brahim Chaib-draa and ,
In Proceedings of the 18th Canadian Conference on
Artificial Intelligence (AI'2005), 2005, (pdf).
Real-Time
Decision Making for Large POMDPs.
Sébastien Paquet, Ludovic Tobin, Brahim Chaib-draa and ,
In Proceedings of the 18th Canadian Conference on
Artificial Intelligence (AI'2005), 2005, (pdf).
2004
Articles in Referred
Proceedings
Les réseaux
d'engagements comme méthode pour modéliser le
comportement dialogique des agents.
Mathieu Bergeron, Brahim Chaib-draa and , In Actes des
JFSMA 2004, 251--264, 2004, (pdf).
From Global
Selective Perception to Local Selective Perception.
Nicolas Bernier, Sébastien Paquet, Brahim Chaib-draa and ,
In Proceedings of the 3rd International Joint
Conference on Autonomous Agents & Multiagent Systems
(AAMAS'2004), Nicolas R. Jennings and Carles Sierra
and Liz Sonenberg and Milind Tambe, 1352--1353, 2004, (pdf).
Selective
Perception Learning for Tasks Allocation.
Nicolas Bernier, Sébastien Paquet, Brahim Chaib-draa and ,
In Proceedings of the AAMAS-04 Workshop on Learning
and Evolution in Agent Based Systems, 42--47, 2004,
(pdf).
A Logical
Model for Commitment and Argument Network for Agent
Communication.
Jamal Bentahar, Bernard Moulin, John-Jules Ch. Meyer,
Brahim Chaib-draa and , In Proceedings of the 3rd
International Joint Conference on Autonomous Agents
& Multiagent Systems (AAMAS'2004), Nicholas R.
Jennings and Carles Sierra and Liz Sonenberg and Milind
Tambe, 792--799, 2004, (pdf).
Commitment and Argument
Network: A New Formalism for Agent Communication.
Jamal Bentahar, Bernard Moulin, Brahim Chaib-draa and , In
Advances in Agent Communication, F. Dignum,
146--165, 2004, (pdf).This paper proposes a formal
framework which offers an external representation of
conversations between conversational agents. Using this
formalism allows us: (1) to represent the dynamics of
conversations between agents; (2) to analyze
conversations; (3) to help autonomous agents to take part
in consistent conversations. The proposed formalism,
called “commitment and argument network”, uses a combined
approach based on commitments and arguments. Commitments
are used to capture the social and the public aspect of
conversations. Arguments on the other side are used to
capture the reasoning aspect. We also propose a layered
communication model in which the formalism and the
approach take place.
A Persuasion
Dialogue Game based on Commitments and Arguments.
Jamal Bentahar, Bernard Moulin, Brahim Chaib-draa and , In
Proceedings of the AAMAS-04 First International
Workshop on Argumentation in Multi-Agent Systems,
Iyad Rahwan and Pavlos Moraitis and Chris Reed, 148--164,
2004, (pdf).
A Pragmatic
Approach to Build Conversation Protocols using Social
Commitments.
Roberto A. Flores, Robert C. Kremer and , In Proceedings
of the 3rd International Jiont Conference on Autonomous
Agents & Multiagent Systems (AAMAS'2004),
Nicholas R. Jennings and Carles Sierra and Liz Sonenberg
and Milind Tambe, 1242--1243, 2004, (pdf).
Conversational
Semantics with Social Commitments.
Roberto A. Flores, Philippe Pasquier, Brahim Chaib-draa
and , In Proceedings of the Workshop on Agent
Communications at the 3rd International Joint Conference
on Autonomous Agents and Multiagent Systems, Rogier
van Eijk and Marc-Philippe Huget and Frank Dignum, 19--33,
2004, (pdf).
A Principled Modular Approach
to Construct Flexible Conversation Protocols.
Roberto A. Flores, Robert C. Kremer and , In Advances
in Artificial Intelligence: 17th Conference of the
Canadian Society for Computational Studies of
Intelligence, Canadian AI 2004, A.Y. Tawfik and S.D.
Goodwin, 1--15, 2004, (pdf).Building conversation protocols has
traditionally been an art more than a science, as their
construction is often guided by designers' intuition
rather than by a principled approach. In this paper we
present a model for building conversation protocols using
inference principles that allow the computational
speci?cation and veri?cation of message sequencing and
turn-taking. This model, which is based on the negotiation
of social commitments, results in highly ?exible protocols
that support agent heterogeneity while abiding by software
engineering practices. We exemplify the speci?cation of
protocols using the contract net protocol, a common
interaction protocol from the multiagent literature.
A
Decentralized Approach to Collaborative Driving
Coordination.
Simon Hallé, Julien Laumonier, Brahim Chaib-draa and , In
Proceedings of the 7th IEEE International Conference on
Intelligent Transportation Systems (ITSC'2004),
2004, (pdf).
Collaborative
Driving System Using Teamwork for Platoon Formations.
Simon Hallé and Brahim Chaib-draa, In Proceedings of
the AAMAS-04 Workshop on Agents in Traffic and
Transportation, 35--46, 2004, (pdf).
Resource
Allocation in Time-Constrained Environments: The Case of
Frigate Positioning in Anti-Air Warfare.
Jean-Francois Morissette, Brahim Chaib-draa, Pierrick
Plamondon and , In Proceedings of the 5th
International Conference on Computer Science Modelling,
Computation and Optimization in Information Systems and
Management Sciences (MCO'2004), Le Thi Hoai An and
Pham Dinh Tao, 463--470, 2004, (pdf).
Multi-Agent
Simulation of Collaborative Strategies in a Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proceedings of the 3rd International Jiont
Conference on Autonomous Agents & Multiagent Systems
(AAMAS'2004), Nicholas R. Jennings and Carles Sierra
and Liz Sonenberg and Milind Tambe, 52--59, 2004, (pdf).
An Agent
Simulation Model for the Québec Forest Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proc. 8th International Workshop on Cooperative
Information Agents (CIA2004), Matthias Klusch and
Sascha Ossowski and Vipul Kashyap and Rainer Unland,
226--241, 2004, (pdf).
Multi-Attribute
Decision Making in a Complex Multiagent Environment
using Reinforcement Learning with Selective Perception.
Nicolas Bernier Sébastien Paquet, Brahim Chaib-draa and ,
In Advances in Artificial Intelligence: 17th
Conference of the Canadian Society for Computational
Studies of Intelligence, Canadian AI 2004, A.Y.
Tawfik and S.D. Goodwin, 416--421, 2004, (pdf).
Comparison of
Different Coordination Strategies for the RoboCupRescue
Simulation.
Sébastien Paquet, Nicolas Bernier, Brahim Chaib-draa and ,
In Proceedings of The 17th International Conference on
Industrial & Engineering Applications of Artificial
Intelligence & Expert Systems, 987--996, 2004, (pdf).
A
Computational Model for Conversation Policies for Agent
Communication.
Jamal Bentahar, Bernard Moulin, John-Jules Ch. Meyer,
Brahim Chaib-draa and , In Proceedings of 5th
International Workshop on Computational Logic in
Multi-Agent Systems (CLIMA V), 2004, (pdf).
Multi-Platform
Coordination in Command and Control.
Patrick Beaumont, Brahim Chaib-draa and , In Proceedings
of the 3rd International Conference on Knowledge Systems
for Coalition Operations (KSCO'2004), 2004, (pdf).
The use of agent and multiagent
techniques to assist human in its daily routine has been
increasing for many years, notably in Command and Control
(C2) systems. In this article, we focused on multiagent
coordination techniques for resources management in
realtime C2 systems. The particular problem we studied is
the design of a decision-support for anti-air warfare on
Canadian frigates. In the case of the several frigates
defending against incoming threats, multiagent
coordination is a complex problem of capital importance.
Better coordination mechanisms are important to avoid
redundancy in engagements and inefficient defence caused
by conflicting actions. We present different task sharing
coordination mechanisms with their evaluation.
2003
Articles in Referred Journals
Modèle des
dialogues entre agents : un état de l'art.
Philippe Pasquier, Brahim Chaib-draa and , In In
Cognito, Cahiers Romans de Sciences Cognitives,
77--135, 2003, (pdf).
Book Chapters
Car platoons
simulated as a multiagent system.
Simon Hallé, Brahim Chaib-draa, Julien Laumonier and , In
Proceedings of the 4th Workshop on Agent-Based
Simulation, SCS, 2003, (pdf).
Agent-Based
Simulation of the Amplification of Demand Variability in
a Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proceedings of the 4th Workshop on Agent-Based
Simulation, SCS, 2003, (pdf).
Edited Books
Modèles
formels de l'interaction.
A. Herzig, B. Chaib-draa and , Cépadues, 2003 (bib).
Articles in Referred
Proceedings
Coalition
Formation with Non-Transferable Payoff for Group Buying.
Frederick Asselin and Brahim Chaib-draa, In Proceedings
of the Second International Joint Conference on
Autonomous Agents and Multiagent Systems (AAMAS'03),
Jeffrey S. Rosenschein and Tuomas Sandholm and Michael
Wooldridge and Makoto Yokoo, 922--923, 2003, (pdf).
Towards a
Formal Framework for Conversational Agents.
Jamal Bentahar, Bernard Moulin, Brahim Chaib-draa and , In
Proceedings of the Agent Communication Languages and
Conversation Policies AAMAS 2003 Workshop,
Marc-Philippe Huget and Frank Dignum, 2003, (pdf).
Vers une
approche pour la modélisation du dialogue basée sur les
engagements et les arguments.
Jamal Bentahar, Bernard Moulin, Brahim Chaib-draa and , In
Actes des Secondes Journées Francophones Modèles
Formels de l'Interaction, Andreas Herzig and Brahim
Chaib-draa and Philippe Mathieu, 19--28, 2003, (pdf).
DIAGAL: A Tool
for Analyzing and Modelling Commitment-Based Dialogues
between Agents.
Marc-André Labrie, Brahim Chaib-draa, Nicolas Maudet and ,
In Advances in Artificial Intelligence, Yang Xiang
and Brahim Chaib-draa, 353--369, 2003, (pdf).
Proceedings of the 16th Conference of the Canadian Society
for Computational Studies of Intelligence (AI 2003).
Multi-Agent Coordination
Based on Tokens: Reduction of the Bullwhip Effect in a
Forest Supply Chain.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proceedings of the 2nd International Joint
Conference on Autonomous Agents and MultiAgent Systems
(AAMAS'03), Jeffrey S. Rosenschein and Tuomas
Sandholm and Michael Wooldridge and Makoto Yokoo,
670--677, 2003, (pdf).
In this paper, we focus on the
supply chain as a multi-agent system and we propose a new
coordination technique to reduce the uctuations of orders
placed by each company to its suppliers in such a supply
chain. This problem of amplication of the demand
variability is called the bullwhip effect. To reduce such
a bullwhip effect, we propose a technique based on tokens
to achieve a decentralized coordination. Precisely,
classical orders manage the demand itself whereas tokens
manage effects on company inventory due to variations of
this demand. Finally, the proposed approach is validated
by the Wood Supply Game, which is a supply chain model
used to make players aware of the bullwhip effect. We
experimentally verify that our coordination technique
leads to less variable orders (i.e. the standard deviation
of orders is reduced) while inventory levels are not
excessively high but sufcient to avoid backorders.
Satisfaction
distribuée de contraintes et son application à la
génération d'un emploi du temps d'employés.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proceedings of the 5th Congrès International de
Génie Industriel (CIGI), 2003, (pdf).
Coordination à
base de jetons pour réduire l'amplification de la
variabilité de la demande dans une chaine logistique.
Thierry Moyaux, Brahim Chaib-draa, Sophie D'Amours and ,
In Proceedings of the 5e Congrès International de
Génie Industriel (CIGI), 2003, (pdf).
Learning
Coordination in RoboCupRescue.
Sébastien Paquet and , In Advances in Artificial
Intelligence, Yang Xiang and Brahim Chaib-draa,
627--628, 2003, (pdf).
Proceedings of the 16th Conference of the Canadian Society
for Computational Studies of Intelligence (AI 2003).
The Cognitive
Coherence Approach for Agent Communication Pragmatic.
Philippe Pasquier, Brahim Chaib-draa and , In Proceedings
of the 2nd International Joint Conference on Autonomous
Agents and MultiAgent Systems (AAMAS'03), Jeffrey S.
Rosenschein and Tuomas Sandholm and Michael Wooldridge and
Makoto Yokoo, 544--551, 2003, (pdf).
An Exploration in Using
Cognitive Coherence Theory to Automate BDI Agents'
Communicational Behavior.
Philippe Pasquier, Nicolas Andrillon, Brahim Chaib-draa
and , In Proceedings of the Agent Communication
Languages and Conversation Policies AAMAS 2003 Workshop,
Marc-Philippe Huget and Frank Dignum, 2003, (pdf). The cognitive coherence theory for
agent communication pragmatics allows modelling a great
number of agent communication aspects while being
computational.This paper describe our exploration in
applying the cognitive cohorence pragmatic theory for BDI
agents communication. The presented pratical framework
rely on your dialogue games based agent communication
language (DIAGAL) and our dialogue game simulator toolbox
(DGS).It provides the necessary theoritical and pratical
elements for implementing the theory as a new layer over
classical BDI agents. In doing so, it brought a general
scheme for automatizing agent's communication behavior.
Finally, we give an example of the resulting system
executing.
Engagements,
intentions et jeux de dialogue.
Philippe Pasquier, Brahim Chaib-draa and , In Actes
des Secondes Journées Francophones Modèles Formels de
l'Interaction, Andreas Herzig and Brahim Chaib-draa
and Philippe Mathieu, 289--294, 2003, (pdf).
A Frigate
Movement Survival Agent-Based Approach.
Pierrick Plamondon, Brahim Chaib-draa, Patrick Beaumont,
Dale Blodgett and , In Proceedings of the 7th
International Conference on Knowledge-Based Intelligent
Information & Engineering Systems (KES'2003), V.
Palade and R.J. Howlett and L.C. Jain, 683--691, 2003, (pdf).
Request for
Action Reconsidered as a Dialogue Game based on
Commitments.
Brahim Chaib-draa, Marc-André Labrie, Nicolas Maudet and ,
In Communication in Multiagent Systems,
Marc-Philippe Huget, 284--299, 2003, (pdf).
Proceedings
Advances in
Artificial Intelligence.
Y Xiang and B. Chaib-draa, 16th Conf. of the Canad.
Society for Comput. Studies of Intelligence (AI-2003),
2003 (bib).
2002
Articles in Referred Journals
Multi-items Auctions for
Automatic Negotiation.
Houssein Benameur, Brahim Chaib-draa, Peter Kropf and , In
Journal of Information and Software Technology,
291--301, 2002, (pdf).Available resources can often be
limited with regard to the number of demands. In this
paper we propose an approach for solving this problem
which consists of using the mechanisms of multi-item
auctions for allocating the resources to a set of software
agents. We consider the resource problem as a market in
which there are vendor agents and buyer agents which trade
on items representing the resources. These agents use
multi-item auctions which are viewed here as a process of
automatic negotiation, and implemented as a network of
intelligent software agents. In this negotiation, agents
exhibit different acquisition capabilities which let them
act differently depending on the current context or
situation of the market. For example, the ?richer? an
agent is, the more items it can buy, i.e. the more
resources it can acquire.We present a model for this
approach based on the English auction, then we discuss
experimental evidence of such a model.
Trends in Agent Communication
Language.
Brahim Chaib-draa, Frank Dignum and , In Computational
Intelligence, 89--101, 2002, (pdf).This article aims to present the
agent reasoning paradigm which is usually implicit beyond
the new social approaches for agent communication. In
order to propose a pragmatic of agent communication with
those approaches, we provide a link between public/social
aspects and private cognitions (resulting in intentions).
Finally we indicate how those links could be use to
automatize DIAGAL's dialogue games use.
Causal Maps: Theory,
Implementation and Practical Applications in Multiagent
Environments.
Brahim Chaib-draa and , In IEEE Transactions on
Knowledge and Data Engineering, 1--17, 2002, (pdf).Analytical techniques are generally
inadequate for dealing with causal interrelationship among
a set of individual and social concepts. Usually, causal
maps are used to cope with this type of
interrelationships. However, the classical view of causal
maps is based on an intuitive view with ad hoc rules and
non precise semantics of the primitive concepts, nor a
sound formal treatment of relations between concepts. In
this paper, we solve this problem by proposing a formal
model for causal maps with a precise semantics based on
relation algebra and the sofware tool, CM-RELVIEW, in
which it has been implemented. Then, we investigate the
issue of using this tool in multiagent environements by
explaining through different examples how and why this
tool is usful for the following aspects: 1)the reasonning
on agents'subjective view, 2)the qualitative distributed
decision making, and 3)the organization of agents
considered as a holistic approach. For each of these
aspects, we focus on the computationnal mechanism
developped within CM-RELVIEW to support it.
L'interaction comme champ de
recherche.
Brahim Chaib-draa, Robert Demolombe and , In Information-Interaction-Intelligence,
2002, (pdf).Cette pr?eface vise a faire un
tour d'horizon de la probl?ematique de l'interaction au
travers particulierement des questions suivantes : (1)
qu'entend-on par interaction ? ; (2) quels sont les
concepts primitifs de l'interaction ? (3) quels
formalismes convient-il d'utiliser dans le cadre de
l'interaction ? Avec ce tour d'horizon, nous esp?erons
faciliter la compr?ehension de ce num?ero sp?ecial d?edi?e
auxModeles formels de l'interaction, une s?erie
d'articles s?electionn?es a partir des “premieres
journ?ees d'?etudes sur les modeles formels de
l'interaction” tenues a Toulouse du 21 au 23 mai 2001.
Commitment-based and
Dialogue-game based Protocols--News Trends in Agent
Communication Language.
Nicolas Maudet, Brahim Chaib-draa and , In Knowledge
Engineering, 157--179, 2002, (pdf).This survey introduces existing
approaches to agent communications languages (ACLs) and
particularly, converation policies (CPS) which can be
viewed as general constraints on the sequence of
semantically coherent messages leading to a goal. Then
limitations of these CPs are discussed in details
paricularly limitations on flexibility and specification.
Finally ACLs are viewed from the dialectic point of view
and some approaches are introduced in this context: some
focusing on commitment-based protocols and others on
dialogue based protocols.
Book Chapters
Aspects
formels des systèmes multiagents.
B. Chaib-draa, L. Gaguet and , In Organisation et
applications des SMAS, Hermes Lavoisier, 2002 (bib).
Articles in Referred
Proceedings
A Method to
Optimize Ship Maneuvers for the Coordination of Hardkill
and Softkill Weapons within a Frigate.
Dale Blodgett, Pierrick Plamondon, Brahim Chaib-draa,
Peter Kropf, Eloi Bosse and , In 7th International
Command and Control Research and Technology Symposium
(7th ICCRTS), 2002, (pdf).
The coordination of anti-air
warfare hardkill and softkill weapon systems is an
important aspect of command and control for a Frigate.
Since the effectiveness of a particular weapon varies
depending on the orientation of the Frigate with respect
to the threats faced, a key element of the coordination
process is to maneuver the Frigate to most effectively use
all the weapons available. This paper shows that the
environment surrounding the Frigate can be divided into
six fundamental sectors for weapon engagement. The method
to determine the general effectiveness of each sector for
the threats faced is shown. A na?ve Bayes method that
determines the optimal positioning of the Frigate to most
effectively use the hardkill and softkill weapons is
presented. Also discussed are the different types of
planners that were investigated for planning engagements
for the hardkill and softkill weapon systems. Preliminary
results comparing and rating these planners are shown,
both with and without the recommended maneuvers.
Toward a
Protocol for the Formation of Coalitions of Buyers.
Frederick Asselin, Brahim Chaib-draa and , In Proceedings
of the Fifth International Conference on Electronic
Commerce Research (ICECR-5), Teodor Gabriel Crainic
and Bezalel Gavish, 2002, (pdf).
Towards an
Agent-Based Approach for Multimarket Package
e-Procurement.
Houssein Ben-Ameur, Stéphane Vaucher, Robert Gérin-Lajoie,
Peter Kropf, Brahim Chaib-draa and , In Proceedings of
the Fifth International Conference on Electronic
Commerce Research (ICECR-5), Teodor Gabriel Crainic
and Bezalel Gavish, 2002, (pdf).
Cohérence et
conversations entre agents : vers un modèle basé sur la
consonance cognitive.
Philippe Pasquier and Brahim Chaib-draa, In Systèmes
multi-agents et systèmes complexes : ingénierie,
résolution de problèmes et simulation, Philippe
Mathieu and Jean-Pierre Müller, 189--203, 2002, (pdf).
Actes des JFIADSMA'02.
Request for
Action Reconsidered as Dialogue Game based on
Commitments.
Nicolas Maudet, Brahim Chaib-draa and Marc-André Labrie,
In Workshop on Agent Communication Languages and
Conversation Policies (at AAMAS´02), Huget, M.-P.,
284--299, 2002 (bib).
2001
Articles in Referred Journals
NetSA: une architecture
multiagent réutilisable pour les environnements riches
en informations.
M. Coté, B. Chaib-draa, N. Troudi and , In
Information, Interaction, Intelligence , 39--78,
2001, (pdf).Today, the web requires software
that can operate on heterogenous sources of information
which are generally located in an open and dynamic
environment (e.g., the internet). It also requires to
reconsider some complex applications (as for instance,
digital libraries, bank services, insurance services,
etc.) as a set of autonomous agents having the ability to
achieve some objectives. In this paper, we propose a
reusable multiagent architecture integrating new
technologies (as for instance: agent technology, KQML,
Mediation between agents, web technology, etc.) that help
to support such requirements. This architecture, called
NetSA, has three levels: the first is devoted to the
communication with users, the second deals with
information and the third is in charge of extraction and
queries of information. Thus, NetSA addresses issues as
(i) interaction between agents and agents/users, (ii)
reasoning for the mediation between agents and finally,
(iii) information through its interaction with legacy
systems. We have specified, developed and validated such
architecture. To validate it, we have opted for a
competition between three banks so that they offer the
best mortgage to users. To achieve that, we have studied
and developed algorithms from auction theory that can
optimize sellers or buyers according to some conditions.
Our first results have shown that NetSA (a) can be
accessible by a maximum of 15 users (for more users, it
might be useful to use many NetSA architectures); (b)
offers an efficient information seeking than with
classical tools; (c) is easy to use; (d) is very useful if
one wants to access to legacy systems.
Book Chapters
Agent et
Systèmes Multiagents.
B. Chaib-draa, B. Moulin and I. Jarras, In Principes
et architecture des systèmes multi-agent, Hermes
Lavoisier, 2001 (bib).
Causal
Reasoning in Multiagent Environments.
B. Chaib-draa, In Encyclopedia of Microcomputers,
2001 (bib).
CM-RELVIEW: A
Tool for Causal Reasoning in Multiagent Environments.
B. Chaib-draa, In Encyclopedia of Computer Science and
Technology, 2001 (bib).
Articles in Referred
Proceedings
Vers un outil d'analyse et de
synthèse pour les systèmes multiagents basé sur
l'algèbre relationnelle.
B. Chaib-draa and , In Proc. of Modèles Formels pour
l'Interaction, 2001, (pdf).Dans le cadre des syst?mes
multiagents (SMAs), nous utilisons pr?sentement diff?rents
outils formels d'analyse et de synth?se. Ces outils
formels sont bas?s pour la plupart, sur la logique, la
prise de d?cision et les m?canismes de march?. ? notre
avis, ces outils doivent ?tre compl?t?s par le calcul
relationnel si on veut repr?senter et raisonner sur les
structures sociales pour lesquelles, les relations entre
agents sont incontournables. La principale contribution de
ce papier r?side dans l'introduction d'une approche
formelle bas?e sur le calcul relationnel en vue de
raisonner sur les relations aussi bien "enti?res" que
"foues". Les ?l?ments importants que couvre cet article
sont pour l'essentiel : (1) les notions de base du calcul
relationnel ; (2) l'utilisations des relations enti ?res
dans les SMAs et, (4) l'utilisation des relations oues
dans les SMAs.
Révision des croyances dans
un environnement multiagent : une approche basée sur la
crédibilité et les arguments.
I. Jarras, B. Chaib-draa and , In Proc. of Modèles
Formels pour l'Interaction, 2001, (pdf).Peu de recherches se sont pench?es
sur la probl?matique de la r?vision des croyances dans un
cadre multiagent. En tout cas, ? notre connaissance,
aucune ne s'est pench?e sur la r?vision des croyances
tenant compte de la cr?dibilit? des informateurs, tout en
gardant trace des arguments en faveur de la r?vision, une
fois celle-ci effectu?e. C'est ce probl?me qui nous a
motiv? et pour lequel, nous proposons ici, une approche
formelle bas?e sur la logique ?tiquet?e.
CM-RELVIEW: A Tool for Causal
Resoning in Multiagent Environments.
B. Chaib-draa and , In Proc. of Intelligent Agent
Technology-IAT'01, 2001, (pdf).Analytical techniques are generally
inadequate for dealing with causal interrelationships
among a set of individual and social concepts. In this
paper, we present a software tool called CM-RELVIEW based
on relational algebra for dealing with such causal
interrelationships. Then we investigate the issue of using
this tool in multiagent environments, particularly in the
case of: (1) the qualitative distributed decision making
and, (2) the organization of agents considered as a
wholistic approach. For each of these aspects, we focus on
the computational mechanisms developed within CMRELVIEW to
support it.
Automated Negotiation based
on Multi-Items Auctions.
Ben-Ameur Houssein, B. Chaib-draa, P. Kropf and , In Proc.
of Agent Oriented Information Systems-AOIS'01, 2001,
(pdf).Available resources can often be
limited with regard to the number of demands. In this
paper we propose an approach for solving this problem
using the mechanisms of multi-item auctions for allocating
the resources to a set of software agents. We consider the
resource allocation problem as a market with vendor and
buyer agents participating in a multi-item auction. The
agents exhibit different acquisition capabilities which
let them act differently depending on the current context
or situation of the market. We present a model for this
approach based on the English auction, and discuss
experimental evidence of such a model.
A teamwork test-bed for a
decision support system.
Brahim Chaib-draa, Peter Kroft, Sébastien Paquet and , In
Proceedings of EUROSIM 2001: Shaping Future with
Simulation, 2001, (pdf).Resource management in complex
socio-technical systems (as management and control (road,
rail, sea, air), industrial engineering systems,
transportation logistics, etc.) is a central and crucial
process. The many diverse components involved together
with various constraints such as real-time conditions make
it impossible to devise exact optimal solutions. In this
article, we present an approach to the resource management
problem based on the multi-agent paradigm to be applied in
the context of a shipboard command and control (C2)
system. A general architecture for multi-agent planning
and scheduling for achieving a common shared goal together
with a real-time simulation environment as well as a
simulation test-bed using the agent teamwork approach is
described.
Coordinating Plans for Agents
Performing AAW Hardkill and Softkill for Frigates.
Dale Blodgett, Sébastien Paquet, Pierrick Plamondon, Peter
Kropf and , In Proceedings of The 2001 AAAI Fall
Symposium Series, 2001, (pdf).The coordination of anti-air
warfare (AAW) hardkill (HK) and softkill (SK) weapon
systems is an important aspect of command and control for
the HALIFAX Class Frigate. This led to the development of
a rapid prototyping environment, described here, which
supports the investigation of methods to coordinate the
plans produced by AAW HK and SK agents. The HK and SK
planning agents are described. An overview of agent
coordination methods is provided, with a focus on our
initial approach to HK and SK coordination via a Central
Coordinator. This approach was successfully implemented,
and proved effective in mitigating interference between HK
and SK actions, and improved the overall survivability of
the Frigate. Finally, future directions of this research
are presented.
Hyper-Game
Analysis in Multi-Agent Systems.
B. Chaib-draa, In AAAI Spring Symp. on Game and
Decision Making in Multiagent Systems, 2001 (bib).
2000
Articles in Referred
Proceedings
Resource Management in
Socio-Technical Systems: A Multi-Agent Coordination
Framework.
P. Kropf, B. Chaib-draa, B.A. Chalmers and , In HMS
2000, 65--70, 2000, (pdf).Resource management in complex
socio-technical systems is a central and crucial task. The
many diverse components involved together with various
constraints such as real-time conditions make it
impossible to devise exact optimal solutions. In this
article, we present an approach to the resource management
problem based on the multiagent paradigm to be applied in
the context of a shipboard command and control (C2)
system. A general architecture for multiagent planning and
scheduling for achieving a common shared goal together
with a real-time simulation environment as well as a
simulation test-bed using the agent teamwork approach is
described.
1999
Book Chapters
Analyse et
modélisation des discours: des conversations humaines
aux interactions entre agents logiciels.
B. Moulin, S. Delisle, B. Chaib-draa and , In Analyse
et simulation de conversations, L'interdisciplinaire
informatique, 1999 (bib).
Edited Books
Analyse et
simulation de conversations.
B. Moulin, S. Delisle, B. Chaib-draa and ,
L'interdisciplinaire informatique, 1999 (bib).
Articles in Referred
Proceedings
A Simulation Approach based
on Negotiation and Cooperation between Agents: A case
Study.
K. Fisher, B. Chaib-draa, et al. and , In IEEE Trans.
on Systems, Man, and Cybernetics, 531--545, 1999, (pdf).Abstract?This paper begins by
presenting AGENDA, a simulation tool developed for the
simulation and design of applications involving
interacting entities. This testbed consists of two
different levels, the architecture level and the
system-development level. The architecture level describes
a methodology for designing software agents by providing
several important functionalities an agent should have. On
the other hand, the system-development level provides the
basic knowledge-representation formalism, general
inference mechanisms, and simulation tool-box supporting
visualization and monitoring of agents. Following this,
the applicability of AGENDA to the transportation domain
is presented in detail. The main challenge of AGENDA in
the context of this domain has been to provide different
cooperation-scalable methods based on negotiation, leading
to different scheduling mechanisms, and to experimentally
evaluate these mechanisms. This evaluation shows that: 1)
AGENDA is suitable for the realistic application of the
transportation domain; 2) mechanisms used for the vertical
negotiation (between trucks considered as agents) and for
the horizontal negotiation (between companies considered
as agents) are applicable for the real-world application
of the transportation domain. Finally, a complete study of
the scalability of the simulation tool and the algorithms
used for the negotiation is presented. This study, with
the evaluation of the different mechanisms, can help
designers of transportation companies, particularly in the
case of large companies.
Conversations are Social
Activities.
B. Chaib-draa and , In Worshop on Agent Communication
Language, ACL'99, 1999, (pdf).This paper proposes to see agent
communication language (ACL) as a joint activity and not
as the sum of the speaker's and hearer's (speech) acts. In
this paper, a conversation in the context of ACL is viewed
as a joint activity which can be realized as sequences of
smaller actions, many of which are themselves joint
actions. Social agents which participate to this joint
activity have to coordinate their joint actions. In each
joint act, the participants face a coordination problem:
which actions are expected? The answer to this question
proposed here, is based on complex notions as collective
intention, joint plan, joint commitments and the notion of
common ground.
CM-RELVIEW: A
Tool for Causal Reasoning in Multiagent Environments.
B. Chaib-draa, In PRIMA Conf. on Multiagent Systems,
1999 (bib).
1998
Articles in Referred Journals
A Relational
Model of Cognitive Maps.
B. Chaib-draa and J. Desharnais, In International
Journal of Human-Computer Studies, 181--200, 1998, (pdf).
Aspects
Statiques et Dynamiques des Croyances.
S. Djeffal, B. Chaib-draa and , In Revue
d'Intelligence Artificielle, 103--123, 1998, (pdf).
NetSA, Une
Architecture Multiagent pour la Recherche sur Internet.
Marc Coté et Nader Troudi. and , In Expertise
Informatique, 1998, (pdf).
Articles in Referred
Proceedings
Agent Communication Language:
A Semantics based on the Success, Satisfaction and
Recursion.
B. Chaib-draa, D. Vanderveken and , In Proc. Agent
Theories, Archit. and Lang., 1998, (pdf).Searle and Vanderveken's model of
speech acts is undoubtedly an adequate model for the
design of communicating agents because it oers a rich
theory which can give important properties of protocols
that we can formalize properly. We examine this theory by
focusing on the two fundamentals notions, success and
satisfaction, which represent a systematic, uni ed account
of both the truth and the success conditional aspects.
Then, we propose an adequate formalism{the situation
calculus{for representing these two notions (in a
recursive way) in the context of agent communication
language. The resulting framework is nally used for (1)
the analysis and interpretation of speech acts; (2) the
semantics and descriptions of agent communication
languages.
Vers des
agents logiciels considérés comme de systèmes logiques :
une approche basée sur la logique LDS.
I. Jarras and B. Chaib-draa, In Actes Journées Jeunes
Chercheurs en Intelligence Artificielle, 1998 (bib).
Modélisation du Raisonnement
Multiagent : une Approche Basée sur les Labels.
I. Jarras, Chaib-draa and , In Actes des 6èmes
Journées Francophones en IA distribuée et Systèmes
Multiagents, JFIAD-98, 1998, (pdf).Devant l?int?r?t sans cesse
grandissant aux syst?mes multiagents durant cette derni?re
d?cade, le d?veloppement d?outils formels pour l?analyse,
la description et l?implantation de ces syst?mes est,
aujourd?hui, plus que n?cessaire. La plupart des m?thodes
formelles d?velopp?es jusqu?? date sont bas?es sur la
s?mantique des mondes possibles. Cette derni?re bien
qu??l?gante est handicap?e par deux grands probl?mes : 1)
le probl?me de l?omniscience et 2) le probl?me de
m?canisation. Dans notre approche, un agent est d?fini
comme ?tant un syst?me LDS muni d?un ensemble de
m?canismes comme l?action, l?abduction et la mise ? jour.
Dans le pr?sent article, nous pr?sentons une mod?lisation
d?agents par des syst?mes logiques bas?s sur les LDS
(syst?mes d?ductifs ?tiquet?s) de Gabbay. Le mod?le obtenu
est appliqu? par la suite au probl?me bien connu des n
sages (raisonnement sur autrui).
Une approche
basée sur l'arrière fond conversationnel et l'intention
collective pour les conversations entre agents logiciels.
L. Vongkasem, B. Chaib-draa and , In Actes des 6èmes
Journées Francophones en Intelligence Artificielle
Distribuée & Systèmes Multi-Agents, 1998, (pdf).
A Relational
Modelling of Cognitive Maps.
B. Chaib-draa and , In Advances in Artificial
Intelligence, 12th Biennal Conf. on AI, Mercer R. E.
and E. Neufeld, 1998 (bib).
A useful tool for causal
reasoning is the language of cognitive maps developed by
political scientists to analyse, predict and understand
decisions. Although, this language is based on simple
inference rules and its semantics is ad hoc, it has many
attractive aspects and has been found useful in many
applications: administrative sciences, game theory,
information analysis, popular political developments,
electrical circuits analysis, cooperative man?machines,
distributed group-decision support and adaptation and
learning, etc. In this paper, we show how cognitive maps
can be viewed in the context of relation algebra, and how
this algebra provides a semantic foundation that helps to
develop a computational tool using the language of
cognitive maps. 1998 Academic Press
1997
Articles in Referred Journals
Coordination in CE Systems:
An Approach Based on the Management of Dependencies
Between Agents.
S. Lizotte, B. Chaib-draa and , In CERA: Concurrent
Engineering: Research and Applications, 367--377,
1997, (pdf).Coordination is a crucial problem
in CE systems and it is neither easy to obtain nor to
maintain. Our work is an attempt to develop a general
model for coordination which can be adapted for some
situations in the context of CE. For this purpose, the
coordination denition developed by Malone [25] has been
adopted. Coordination is then dened as the process of
managing dependencies between activities. In this
context, a theoretical model is presented that allows
one to determine how to model an agent's activities and
how to detect dependencies between those activities. In
our model, major concepts are developed in terms of
components of coordination, situations of coordination,
coordination mechanisms and the coordination process. In
this paper, we detail this model and then, we present an
illustrative example and nally, we identify the current
status and the future evolution of our approach.
Articles in Referred
Proceedings
Stratégies de négociation
entre agents dans le domaine du transport.
M. Sassi, B. Chaib-draa and , In Actes des 5èmes Jour.
Franc. en Intelligence Artificielle Distribuée-Systèmes
Multi-Agents, J. Quinquetin and M-C Thomas and B.
Trousse, 279--294, 1997, (pdf). It is generally established that
negotiation and planning problem in the transportation
domain is a complex problem. To contribute to this
problem, we propose in this paper a multi-agent approach.
In this approach, trucks considered as rational and
autonomous "intelligent agents" negotiate their tasks by
selling and buying their tasks. This sort of simulated
trading allows them to reach a compromise which minimise
their global plan. We then associate to the simulated
trading some heuristics (IDA*, Tabu and Simulated
Annealing) to improve it. Results of negotiation between
trucks using this "new" simulated trading are discussed in
details.
Causal
Reasoning in Multiagent Systems.
B. Chaib-draa, In Multi-Agent Rationality, MAAMAW'97,
M. Boman and W. Van de Velde, 1997 (bib).
Database Meet
Distributed AI.
G. Babin, Z. Maamar and B. Chaib-draa, In Fisrt
International Workshop on Cooperative Information Agents
CIA'97, M. Klush, 1997 (bib).
Connection
Between Micro and Macro Aspects of Agent Modeling.
B. Chaib-draa, In Proceedings of Autonomous Agents
AA'97, 1997 (bib).
1996
Articles in Referred Journals
A Design
Methodology For Real-Time Systems to be Implemented on
Multiprocessors Target Machine.
L. Zhang and B. Chaib-draa, In Journal of Systems and
Software, 37--56, 1996 (bib).
Hierarchical
Model and Communication by Signs, Signals and Symbols in
Multiagent Environments.
B. Chaib-draa, P. Levesque and , In Journal of
Experimental and Theoretical AI, 7--20, 1996, (pdf).
Interaction
Between Agents in Routine, Familiar and Unfamiliar
Situations.
Brahim Chaib-draa and , In Inteernational Journal of
Intelligent & Cooperative Information Systems,
1--25, 1996, (pdf).
Evaluation de
diverses méthodes pour des problèmes d'allocation de
règles dans les systèmes de production parallèles.
Hassaine F., B. Chaib-draa and , In Revue
d'Intelligence Artificielle, 496--497, 1996 (bib).
Book Chapters
A Review of
Distributed Artificial Intelligence.
B. Moulin and B. Chaib-draa, In Foundations of
Distributed Artificial Intelligence, Wiley, 3--55,
1996 (bib).
Articles in Referred
Proceedings
Reasoning on
Conflicts and Negotiation Through Causal Maps.
B. Chaib-draa, In Proc. of Sec. Int. Conf . on
Multi-Agent Systems, 1996, Poster (bib).
A Hierarchical
Model of Agent Based on Skill, Rules, and Knowledge.
B. Chaib-draa, In Advances in Artificial Intelligence,
11th Biennal Conf. on AI, McCalla, 1996 (bib).
Structures
relationnelles pour les interactions entre agents.
K. Lechilli and B. Chaib-draa, In Actes des 4èmes
Jour. Franc. en Intelligence Artificielle
Distribuée-Systémes Multi-Agents, 1996 (bib).
1995
Articles in Referred Journals
Industrial
Applications of Distributed AI.
Brahim Chaib-draa, In Communication of ACM,
49--53, 1995, (pdf).
Articles in Referred
Proceedings
Coordination en
situation non familières.
S. Lizotte and B. Chaib-draa, In Actes des 3èmes Journ
ées Francophones en Intelligence Artificielle Distribuée
& Systémes Multi-Agents, 255--266, 1995 (bib).
1994
Book Chapters
Distributed
Artificial Intelligence: An Overview.
B. Chaib-draa, In Encyclopedia of Computer Science and
Technology, 1994 (bib).
Articles in Referred
Proceedings
A Relation
Graph Formulation for Relationships Between Agents.
B. Chaib-draa, J. Desharnais and S. Lizotte, In Proceedings
of 13th International DAI Workshop, 1994 (bib).
Hierarchical
Model and Communication by Signs, Signals and symbols in
Multiagent Environments.
B. Chaib-draa and P. Levesque, In Modeling Autonomous
Agents in a Multi-Agent World, MAAMAW'94, 1994,
Appeared also in Distributed Software Agents and
Applications, Perram J. W. and Müller (eds) (bib).
1992
Articles in Referred Journals
Trends in
Distributed Artificial Intelligence.
Brahim Chaib-draa, Bernard Moulin, R. Mandiau and P.
Millot, In Artificial Intelligence Review, 35--66,
1992 (bib).