Call For Participation

The Seventh Workshop in the MSDM series
June 5, 2012
Valencia, Spain

In sequential decision making, an agent's objective is to choose actions, based on its observations of the world, in such a way that it expects to optimize its performance measure over the course of a series of such decisions. In environments where action consequences are non-deterministic or observations incomplete, Markov Decision Processes (MDPs) and Partially-Observable MDPs (POMDPs) serve as the basis for principled approaches to single-agent sequential decision making. Extending these models to systems of multiple agents has become the subject of an increasingly active area of research over the past decade and a variety of models have emerged (e.g., the MMDP, Dec-POMDP, MTDP, I-POMDP, and POSG). The high computational complexity of these models has driven researchers to develop multiagent planning and learning methods that exploit the structure present in agents' interactions, methods geared towards efficient approximate solutions, and methods that distribute computation among the agents.

The primary purposes of this workshop are to bring together researchers in the field of MSDM to present and discuss new work and preliminary ideas, to identify recent trends in model and algorithmic development, and to establish important directions and goals for further research and collaboration. A secondary goal is to help address an important challenge; in order to make the field more accessible to newcomers, and to facilitate multidisciplinary collaboration, we seek to bring order in the large number of models and methods that have been introduced over the last decade. The workshop also aims to discuss interesting and challenging application areas (e.g., cooperative robotics, distributed sensor and/or communication networks, decision support systems) and suitable evaluation methodologies. In the long term, the active discussions that the MSDM workshop promotes will help us to overcome the challenges of applying multiagent sequential decision making methods to large-scale real-world problems, for instance, in security, sustainability, public safety and health.

Invited Speaker

Prof. Makoto Yokoo (Kyushu University)

Accepted Papers

POMDPs in OpenMarkov and ProModelXML
Manuel Arias, Francisco Javier Díez, Miguel Ángel Palacios-Alonso, Mar Yebra, and Jorge Fernández

Solving Finite Horizon Decentralized POMDPs by Distributed Reinforcement Learning
Bikramjit Banerjee, Jeremy Lyle, Landon Kraemer, and Rajesh Yellamraju

Planning Delayed-Response Queries and Transient Policies under Reward Uncertainty
Robert Cohn, Edmund Durfee, and Satinder Singh

Improved Solution of Decentralized MDPs through Heuristic Search
Jilles Dibangoye, Christopher Amato, and Arnaud Doniec

Automated Equilibrium Analysis of Repeated Games with Private Monitoring: A POMDP Approach
Yongjoon Joe, Atsushi Iwasaki, Michihiro Kandori, Ichiro Obara and Makoto Yokoo

Exploiting Sparse Interactions for Optimizing Communication in Dec-MDPs
Francisco S. Melo, Matthijs Spaan, and Stefan Witwicki

Tree-based Pruning for Multiagent POMDPs with Delayed Communication
Frans Oliehoek and Matthijs Spaan

Strategic Behaviour Under Constrained Autonomy
Zinovi Rabinovich

Prioritized Shaping of Models for Solving DEC-POMDPs
Pradeep Varakantham, William Yeoh, Prasanna Velagapudi, Katia Sycara, and Paul Scerri

Coordinated Multi-Agent Learning for Decentralized POMDPs
Chongjie Zhang and Victor Lesser

Discussion Topics

Multiagent sequential decision making comprises (1) problem representation, (2) planning, (3) coordination, and (4) learning. The MSDM workshop addresses this full range of aspects. Topics of particular interest include:

Organizing Committee
Prashant Doshi University of Georgia
Stefan Witwicki INESC-ID, Instituto Superior Técnico
Jun-young Kwak University of Southern California
Frans A. Oliehoek Maastricht University
Akshat Kumar University of Massachusetts Amherst
Program Committee
Christopher Amato Aptima, Inc.
Raphen Becker Google
Daniel Bernstein University of Massachusetts Amherst
Aurélie Beynier University Pierre and Marie Curie (Paris 6)
Alan Carlin University of Massachusetts Amherst
Brahim Chaib-Draa Laval University
Georgios Chalkiadakis Technical University of Crete
François Charpillet INRIA
Ed Durfee University of Michigan
Alessandro Farinelli University of Verona
Alberto Finzi Universita di Napoli
Claudia Goldman GM Advanced Technical Center Israel
Michail Lagoudakis Technical University of Crete
Janusz Marecki IBM T.J. Watson Research Center
Francisco S. Melo INESC-ID Lisboa
Hala Mostafa BBN Technologies
Abdel-Illah Mouaddib Universit de Caen
Enrique Munoz De Cote INAOE, Mexico
Brenda Ng Lawrence Livermore National Laboratory
Praveen Paruchuri Carnegie Mellon University
David Pynadath University of Southern California
Xia Qu University of Georgia
Zinovi Rabinovich Bar-Ilan University
Anita Raja University of North Carolina at Charlotte
Paul Scerri Carnegie Mellon University
Jiaying Shen SRI International, Inc.
Matthijs Spaan Delft University of Technology
Katia Sycara Carnegie Mellon University
Karl Tuyls Maastricht University
Pradeep Varakantham Singapore Management University
Jianhui Wu Amazon
Makoto Yokoo Kyushu University
Chongjie Zhang University of Massachusetts Amherst
Shlomo Zilberstein University of Massachusetts Amherst