*Dear Colleagues, We cordially invite you to attend the 8th MSDM workshop, which is held in conjunction with AAMAS-2013 (the 12th International Joint Conference on Autonomous Agents and Multiagent Systems), in St. Paul, Minnesota, USA.
It is an excellent opportunity for you to absorb the latest advancements in multiagent sequential decision-making research, and to actively discuss a variety of exciting on-going works. This year, we are extremely fortunate to have Dr. Kobi Gal as invited speaker, as well as Prof. Peter Stone, Prof. Shlomo Zilberstein, and Prof. Milind Tambe as panelists. Due to synergy with ALA (Adaptive Learning Agents workshop, http://swarmlab.unimaas.nl/ala2013), MSDM will be hosting a joint panel with ALA this year. The MSDM workshop will take place on May 7, 2013, preceding the technical program of the AAMAS conference. Please join us and many of others in this community. We look forward to seeing you there! Best regards, The MSDM 2013 Organizers * * ================================================================== CALL FOR PARTICIPATION AAMAS 2013 Workshop Multiagent Sequential Decision Making Under Uncertainty (MSDM) ================================================================== The Eighth Workshop in the MSDM series May 7, 2013, 9:00am - 5:30pm St. Paul, Minnesota, USA http://gaips.inesc-id.pt/~switwicki/msdm2013/ Location & Organization ------------------------------------------ The 8th MSDM workshop is held in conjunction with AAMAS-2013 (the 12th International Joint Conference on Autonomous Agents and Multiagent Systems), in St. Paul, Minnesota, USA. It will take place on May 7, 2013, preceding the technical program of the AAMAS conference. Attending MSDM & AAMAS 2013 ------------------------------------------ For registration, please visit the following link: http://aamas2013.cs.umn.edu/node/41 Please register by March 27, 2013 to ensure early registration rates. Workshop Overview ------------------------------------------ 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., 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 that provide efficient approximate solutions, and methods that distribute computation among the agents. The MSDM workshop serves several purposes. The primary purpose is to bring together researchers in the field of MSDM to present and discuss new work and preliminary ideas. Moreover, we aim to identify recent trends, to establish important directions for future research, and to discuss some of the topics mentioned below such as challenging application areas (e.g., cooperative robotics, distributed sensor and/or communication networks, decision support systems) and suitable evaluation methodologies. Lastly, MSDM seeks to bring researchers from other AAMAS communities together in order to facilitate consensus among different models and methods, thus making the field more accessible to new researchers and practitioners. Accepted Papers ------------------------------------------ Joris Scharpff, Matthijs Spaan, Leentje Volker and Mathijs De Weerdt. Planning under Uncertainty for Coordinating Infrastructural Maintenance João Messias, Matthijs Spaan and Pedro U. Lima. Asynchronous Execution in Multiagent POMDPs: Reasoning over Partially-Observable Events Jason Sleight and Ed Durfee. Organizational Design Principles and Techniques for Decision-Theoretic Agents Landon Kraemer and Bikramjit Banerjee. Rehearsal Based Multi-agent Reinforcement Learning of Decentralized Plans Bikramjit Banerjee and Landon Kraemer. Counterfactual Regret Minimization for Decentralized Planning William Yeoh, Akshat Kumar and Shlomo Zilberstein. Automated Generation of Interaction Graphs for Value-Factored Decentralized POMDPs Pablo Hernandez-Leal, Enrique Muñoz de Cote and L. Enrique Sucar. Opponent Modeling and Planning Against Non-Stationary Strategies Guy Shani, Ronen Brafman and Shlomo Zilberstein. Qualitative Planning under Partial Observability in Multi-Agent Domains Okan Aşık and H. Levent Akin. Solving Dec-POMDPs by Genetic Algorithms: Robot Soccer Case Study Hadi Hosseini, Jesse Hoey and Robin Cohen. A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare Christopher Amato and Frans Oliehoek. Bayesian Reinforcement Learning for Multiagent Systems with State Uncertainty 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: - Challenging conventional assumptions ... model specification: where do the models come from? ... what is an appropriate level of abstraction for decision making? - Novel representations, algorithms and complexity results - Comparisons of algorithms - Relationships between models and their assumptions - Decentralized vs. centralized planning approaches - Online vs. offline planning - Communication and coordination during execution - Computational issues involving... ... large numbers of agents ... large numbers of states, observations and actions ... long decision horizons - (Reinforcement) learning in partially observable multiagent systems - Cooperative, competitive, and self-interested agents - Application domains - Benchmarks and evaluation methodologies - Standardization of software - High-level principles in MSDM: past trends and future directions Organizing Committee ---------------------------------------- Prashant Doshi / University of Georgia Jun-young Kwak / University of Southern California Brenda Ng / Lawrence Livermore National Laboratory Frans A. Oliehoek / Maastricht University Stefan Witwicki / INESC-ID & Instituto Superior Técnico Program Committee ---------------------------------------- Martin Allen / University of Wisconsin - La Crosse Christopher Amato / MIT Bikramjit Banerjee / University of Southern Mississippi Raphen Becker / Google Daniel Bernstein / Fiksu, Inc. Aurélie Beynier / University Pierre and Marie Curie (Paris 6) Alan Carlin / University of Massachusetts Georgios Chalkiadakis / Technical University of Crete François Charpillet / INRIA-Loria Ed Durfee / University of Michigan Alberto Finzi / Università di Napoli Piotr Gmytrasiewicz / University of Illinois Chicago Claudia Goldman / GM Advanced Technical Center Israel Akshat Kumar / IBM Research, India Michail Lagoudakis / Technical University of Crete Francisco Melo / Instituto Superior Técnico & INESC-ID Hala Mostafa / BBN Technologies Abdel-Illah Mouaddib / Universit de Caen Enrique Munoz de Cote / National Inst. of Astrophysics Optics and Electronics Simon Parsons / City University of New York Praveen Paruchuri / Carnegie Mellon University David Pynadath / Institute for Creative Technologies, USC Zinovi Rabinovich / Mobileye Anita Raja / University of North Carolina at Charlotte Paul Scerri / Carnegie Mellon University Jiaying Shen / Nuance Communications Matthijs Spaan / Delft University of Technology Peter Stone / University of Texas at Austin Karl Tuyls / Maastricht University Jianhui Wu / Amazon Ping Xuan / Hewlett-Packard Makoto Yokoo / Kyushu University Chongjie Zhang / University of Massachusetts Shlomo Zilberstein / University of Massachusetts *
_______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
