Coordination of the activities of multiple agents, whether selfish or cooperative, is essential for the viability of any system in which multiple agents must coexist. Learning and adaptation are invaluable mechanisms by which agents can evolve coordination strategies that meet the demands of the environments and the requirements of individual agents.

Researchers in machine learning and adaptive systems have been addressing issues concerned with learning and adapting from past experience, observation, failures, etc. Whereas most of this research has focused on techniques for acquisition and effective use of problem solving knowledge from the viewpoint of a single autonomous agent, a few recent investigations have opened the possibility of application of some of these techniques in multiagent settings. Most of these recent results, however, use existing learning techniques to show that individual agents can respond to the uncertainties inherent in the environment and/or uncertainties imposed by the behavior of other agents.

The goal of this workshop is to focus on research that will address unique requirements for agents learning and adapting to work alongside other agents. Recognizing the applicability and limitations of current machine learning research as applied to multiagent problems as well as developing new learning and adaptation mechanisms particularly targeted to this class of problems will be of particular relevance to this workshop.

We focus on three different ways in which machine learning can be used within a Multi-Agent System. An agent can learn about other agents in order to compete and/or cooperate with them. An agent can learn from other agents, taking advantage of their experiences and incorporating these into its knowledge base. Finally, an agent can learn with (alongside) other agents---sharing, interfering, or helping them as it learns.

We would particularly welcome new insights into these problems from other related disciplines and thus would like to emphasize the inter-disciplinary nature of the workshop. Among others, papers of the following kind are welcome:

1. Benefits of adaptive/learning agents over agents with fixed behavior in multiagent problems.
2. Evaluation of the effectiveness of individual learning strategies (e.g., case-based, explanation-based, inductive), or multistrategy combinations, in the context of multiagent problems.
3. Characterization of learning and adaptation methods in terms of modeling power, communication abilities, knowledge requirement, processing abilities of individual agents.
4. Developing learning and adaptation strategies, or reward structures, for environments with cooperative agents, selfish agents, partially cooperative (will cooperate only if individual goals are not sacrificed) and for environments that can contain mixture of these types of agents.
5. Analyzing and constructing algorithms that guarantee convergence and stability of group behavior.
6. Analyzing effects of knowledge acquisition mechanism on responsiveness of agents or groups to addition/deletion of other agents from the environment.
7. Study of adaptive behavior in team games, where one group of cooperative agents are pitted against another group of cooperative agents.
8. Inter-disciplinary research on multi-agent learning and adaptation (including, but not limited to, research in organizational theory, psychology, sociology, and economics).
9. Co-evolving multiple agents with similar/opposing interests.
10. Investigation of teacher-student relationships among agents.

The workshop is open to all members of the AI community but the number of participants will be limited. Participants will be selected by the committee based on the quality of their submitted papers. Those wishing to attend without submitting a paper are welcomed to send a one page abstract stating their interests as they relate to the workshop. All participants must register for the main IJCAI conference.

IMPORTANT DATES

SUBMISSION PROCEDURE

Authors should submit a full paper electronically either as a Postscript, HTML or PDF. In addition, authors should submit an ASCII version of their title page with abstract by email.

To submit your paper, send it's URL to vidal@sc.edu

Submission Format: The first page of submitted papers should include: title, author names, affiliations, postal addresses, electronic mail addresses, telephone and fax numbers for all authors, and a brief abstract. All correspondence will be sent to the author designated as contact person in the electronic title page. Submissions should not exceed 6000 words and should be printed on 8.5" x 11" or A4 paper with at least 1 inch margins on all sides.

ORGANIZING COMMITTEE

PROGRAM COMMITTEE MEMBERS

• Craig Boutilier
• David Carmel
• Manuela Veloso
• Gerhard Weiß

PROCEEDINGS