Held in association with AAMAS 2008, in Estoria, Portugal, May 12-16, 2008.


Click HERE to Register

Accepted Papers and Workshop Schedule

(Click HERE for Proceedings)

9:30 Introduction
9:35 Invited Talk
David Pynadath
10:20 Communicative Opportunity Cost Decentralized MDPs for Improving Agent Coordination
Aurelie Beynier and Abdel-Illah Mouaddib
10:45 A Principled Information Valuation for Communications During Multi-Agent Coordination
Simon A. Williamson, Enrico H. Gerding and Nicholas R. Jennings
11:10 Coffee Break
11:40 Towards Robust Model Identification in Interactive Influence Diagrams Using Mutual Information
Yifeng Zeng and Prashant Doshi
12:05 The MultiAgent Decision Process toolbox: Software for Decision-Theoretic Planning in Multiagent Systems
Matthijs T.J. Spaan and Frans A. Oliehoek
12:30 Discussion 1
1:00 Lunch Break
2:30 Approximately Solving Sequential Games With Incomplete Information
Hala Mostafa and Victor Lesser
2:55 A Study of FMQ Heuristic in Cooperative Multi-Agent Games
Laetitia Matignon, Guillaume J. Laurent and Nadine Le Fort-Piat
3:20 Heuristic Policy Iteration for Infinite-Horizon Decentralized POMDPs
Christopher Amato and Shlomo Zilberstein
3:45 Incremental Pruning Heuristic for Solving DEC-POMDPs
J.S. Dibangoye, AI. Mouaddib and Brahim Chaib-draa
4:10 Coffee Break
4:40 Observation Compression in DEC-POMDP Policy Trees
Alan Carlin and Shlomo Zilberstein
5:05 The Pitfalls of Decision-Theoretic Planning for Real-world Multiagent Systems
Nicolas Stefanovitch, Frederic Peshanski and Amal Seghrouchni
5:30 Not All Agents Are Equal: Scaling up Distributed POMDPs for Agent Networks
Janusz Marecki, Tapana Gupta, Milind Tambe, Pradeep Varakantham and Makoto Yokoo
5:55 Discussion 2 and Closing

Overview

Sequential decision making under uncertainty is the problem an agent faces when it tries to maximize its performance through interacting with its environment (and possibly other agents) based upon its observations of the world. Single-agent decision-theoretic approaches to this problem have centered around two primary models, the Markov Decision Problem (MDP) and the Partially Observable Markov Decision Problem (POMDP), depending on whether the agent's knowledge about the world is complete or partial.

These mathematically rigorous models have been used very successfully in single-agent systems so it is only natural to apply them to systems with many agents. Just as in single-agent decision-theoretic work, the decision-theoretic multi-agent community has focused on two kinds of models: i) where each agent has complete knowledge about the state of the world, and ii) where each agent has partial (and potentially different) knowledge about the state of the world.

The high computational complexity of finding optimal solutions in these multi-agent models has been a significant barrier to applying them to complex real world problems. Much of the work in this area relates to addressing this complexity through exploiting problem structure like locality of interaction, decomposition of reward and independence between the agents, and through approximate algorithms that converge to a local optimum instead of a global optimum.

The purpose of this workshop is to bring together researchers in the field of sequential decision-making in stochastic multi-agent systems to present and discuss promising new work, to discuss the relationships between the various models in use, and to establish important directions and goals for further research and collaboration. This workshop will strive to develop consensus within the community on benchmarks and evaluation methodology in order to contrast the alternative approaches and models, and to study the tradeoffs associated with the use of each. Furthermore, we will discuss the creation of online problem sets for testing the various algorithms to facilitate comparison.

Topics

The workshop will address a range of topics relating to new and existing models of multi-agent systems (i.e. MMDP, Dec-MDP, Dec-POMDP, Dec-MDP-Com, MTDP, COM-MTDP, R-MTDP, E-MTDP, EMT, I-POMDP, POSG, POIPSG, ND-POMDP, TI-Dec-MDP) including:
  • Relationships between the models and their assumptions
  • Algorithms for policy generation and coordination
  • Comparisons of algorithms
  • Distributed vs. centralized planning
  • Online vs. offline planning
  • Communication during policy generation
  • Communication decisions during execution
  • Techniques for scaling problems
  • Identifying subclasses of problems and their complexity
  • Cooperative and competitive agent systems
  • Theoretical and empirical results
  • Benchmarks and evaluation methodologies for comparing different approaches

Important Dates

JANUARY 25, 2008: Workshop paper submission deadline

FEBRUARY 25, 2008: Notification of accepted papers

MARCH 5, 2008: Camera-ready submission

MAY 12, 2008: Day of workshop

Submission Procedure

Authors are encouraged to submit papers up to 15 pages in length in the standard LaTeX Article format (12 pt font). Submissions should be sent to shen@ai.sri.com , in PostScript or PDF form. Each submission will be reviewed by at least two Program Committee members.

Organizing Committee

Rajiv Maheswaran
Computer Science Department and Information Sciences Institute,
University of Southern California
USC-ISI, 4676 Admiralty Way, #1001, Marina Del Rey, CA 90292
Phone: +1 (310) 448-8269
http://cs.usc.edu/~maheswar

Jiaying Shen
Artificial Intelligence Center
SRI International, Inc.
333 Ravenswood Ave., EJ283
Phone: +1 (650) 859-2045
http://www.cs.umass.edu/~jyshen

Pradeep Varakantham
Robotics Institute,
Carnegie Mellon University
NSH 1502, Pittsburgh, PA 15232
Phone: +1 (412) 268-5900
http://www.andrew.cmu.edu/user/varakant/

Program Committee

Raphen Becker Google
Daniel Bernstein
Aurelie Beynier University of Caen
Dmitri Dolgov University of Michigan
Prashant Doshi University of Georgia
Ed Durfee University of Michigan
Alberto Finzi University of Roma
Piotr Gmytrasiewicz University of Illinois--Chicago
Eric Hansen Mississippi State University
Sven Koenig University of Southern California
Victor Lesser University of Massachusetts
Thomas Lukasiewicz University of Roma
Janusz Marecki University of Southern California
Abdel-Illa Mouaddib University of Caen
David Musliner Honeywell Laboratories
Praveen Paruchuri Intelligent Automation
David Pynadath University of Southern California
Zinovi Rabinovich Hebrew University
Anita Raja University of North Carolina at Charlotte
Jeffrey Rosenschein Hebrew University
Maayan Roth Google
Stephen Smith Carnegie Mellon University
Matthijs Spaan Institute for Systems and Robotics - Lisbon
Milind Tambe University of Southern California
Ping Xuan Clark University
Makoto Yokoo Kyushu University
Shlomo Zilberstein University of Massachusetts