AAAI 2004 Fall Symposium on Artificial Multiagent Learning

To be Held	October 21-24, 2004, Washington D.C.
Part of	The AAAI 2004 Fall Symposium Series
Schedule	In Pretty PDF Format. We are strongly considering setting aside a session (likely the last one on Sunday) for any attendies to do quick spiels about their multiagent systems and resarch. If you're interested, please send email to Sean Luke (sean -- at -- cs.gmu.edu). Barring no other proposed activity, we are suggesting that the special activity be a trip to either the Smithsonian Museum of the American Indian or (a long jaunt, but probably worth it) the Smithsonian National Air and Museum Udvar Hazy Center. We'll decide at the workshop.
Friday Oct 22	9-10:30 INTRODUCTION AND COMMENTS Adapting Network Structure for Efficient Team Formation Matthew Gaston, John Simmons, and Marie desJardins Analyzing the Effects of Tags on Promoting Cooperation in Prisoner's Dilemma Austin McDonald and Sandip Sen 11-12:30 Co-Evolving Team Capture Strategies for Dissimilar Robots H. Joseph Blumenthal and Gary Parker Stochastic Direct Reinforcement: Application to Simple Games with Recurrence John Moody, Yufeng Liu, Matthew Saffell, and Kyoungju Youn Dynamics of Strategy Distribution in Iterated Games Stephane Airiau, Sabyasachi Saha, and Sandip Sen 2-6:00 SPECIAL ACTIVITY
Saturday Oct 23	9-10:30 Empirical Comparison of Incremental Learning Strategies for Genetic Programming-Based Keep-Away Soccer Agents Scott Harmon, Edwin Rodriguez, Christopher Zhong, and William Hsu Evolving Control for Micro Aerial Vehicles (MAVs) M. Rhodes, G. Tener, and Annie Wu Learning e-Pareto Efficient Solutions with Minimal Knowledge Requirements Using Satisficing Jacob Crandall and Michael Goodrich 11-12:30 INVITED SPEAKER (MICHAEL LITTMAN) AND JOINT DISCUSSION WITH REAL-TIME REINFORCEMENT LEARNING SYMPOSIUM 2-3:30 Opportunities for Learning in Mulit-Agent Meeting Scheduling Elisabeth Crawford and Manuela Veloso Safe Strategies for Agent Modelling in Games Peter McCracken and Michael Bowling Understanding Competitive Co-evolutionary Dynamics via Fitness Landscapes Elena Popovici and Kenneth De Jong 4-5:30 Learning TOMs: Towards Non-Myopic Equilibria Arjita Ghosh and Sandip Sen Multi-Agent Learning in Conflicting Multi-level Games with Incomplete Information Maarten Peeters, Katja Verbeeck, and Ann Nowe Multi-agent Learning in Mobilized Ad-Hoc Networks Yu-Han Chang and Leslie Pack Kaelbling
Sunday Oct 23	9-10:30 On the Agenda(s) of Research on Multi-Agent Learning Robert Powers and Yoav Shoham Tags and the Evolution of Cooperation in Complex Environments Lee Spector, Jon Klein, and Chris Perry Learning Payoff Functions in Infinite Games Yevgeniy Vorobeychik, Michael Wellman, and Satinder Singh 11-12:30 MULTIAGENT LEARNING PANEL and WRAP-UP
Final Copy Instructions	Final print copy of papers is due on August 31. By that date, please submit: Signed "Permission to Distribute" form Audio/Video request form (if any) A camera-ready copy of the paper to AAAI Follow the following instructions to download forms and submit your paper. The paper must be formatted according to AAAI guidelines. Individual forms and instructions can also be found on the bottom of this page
Topic	Multiagent systems is a subset of distributed artificial intelligence that emphasizes the joint behaviors of agents in environments with some degree of autonomy. In most such environments there are constraints placed on the degree to which any agent may know what other agents know, or on their communication capabilities, such that the system must have distributed control and cannot be solved with a master-slave model via a single master agent. In recent years there has been increasing interest in applying machine learning techniques to multiagent systems problems. The presence of large numbers of agents, increasingly complex agent behaviors, partially observable environments, and the mutual adaptation of agent behaviors make the learning process a challenging one. These problems are further complicated by noisy sensor data, local bandwidth-limited communication, unplanned faults in hardware agents, and stochastic environments. The goal of this symposium is to bring together researchers from diverse areas of the multiagent learning community. Because the dynamics of multiagent learning are such that the correct answer is often not known beforehand, much of the multiagent learning research to date has focused on reinforcement learning or stochastic optimization (evolutionary computation, ant colony optimization, etc.). Some topics of interest include, but are not restricted to: Coevolution and coadaptation Adversarial methods for learning game-playing strategies Swarm and social learning methods Evolutionary game theory Multirobot learning Effects of communication on learning Discovery of emergent behavior Automatic formation of coalitions, contracts, and markets Game-theoretic issues in multiagent learning Agent modeling
Organizing Committee	Michael Bowling (bowling @ cs.ualberta.ca) Kenneth De Jong (kdejong @ cs.gmu.edu) Marie desJardins (mariedj @ cs.umbc.edu) Sean Luke (chair, sean @ cs.gmu.edu) Mitchell Potter (mpotter @ aic.nrl.navy.mil) Lee Spector (lspector @ hampshire.edu)
Related Symposia	We are considering a joint session of some sort with the Real-Life Reinforcement Learning Symposium. For other symposia, see the AAAI Symposia Website.