Adaptation, Coevolution and Learning in Multiagent Systems
Papers from the AAAI Spring Symposium
Coordination of multiple agents is essential for the viability of systems in which these agents share resources. Most of the research in distributed artificial intelligence have concentrated on developing coordination strategies off-line. These pre-fabricated strategies can quickly become inadequate if the system designer's world model is incomplete or incorrect or if the environment can change dynamically. 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.
The goal of this symposium was to focus on research that will address unique requirements for agents learning and adapting to work with 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 these class of problems were of particular relevance to this symposium. Among others, papers of the following kind were welcome:
- Benefits of adaptive/learning agents over agents with fixed behavior in multiagent problems.
- Characterization of methods in terms of modeling power, communication abilities, and knowledge requirement of individual agents.
- Developing learning and adaptation strategies for environments with cooperative agents, selfish agents, partially cooperative agents.
- Analyzing and constructing algorithms that guarantee convergence and stability of group behavior.
- Coevolving multiple agents with similar or opposing interests.
- Interdisciplinary research from fields like organizational theory, game theory, psychology, sociology, economics, etc.