Publications



2017


Articles in Referred Journals

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).

Articles in Referred Proceedings

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


Book

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

Articles in Referred Journals

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).

Articles in Referred Proceedings

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


Articles in Referred Journals

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).

Articles in Referred Proceedings

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 TrottierBrahim 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


Articles in Referred Journals

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).

Articles in Referred Proceedings

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


Articles in Referred Journals

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).

Articles in Referred Proceedings

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


Articles in Referred Journals

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).

Articles in Referred Proceedings

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 payo s 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 di erent 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 o ers 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).