|To be Held||October 21-24, 2004, Washington D.C.|
|Part of||The AAAI 2004 Fall Symposium Series|
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||
|Saturday Oct 23||
|Sunday Oct 23||
|Final Copy Instructions||
Final print copy of papers is due on August 31. By that date, please submit:
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
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:
|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.|