AAAI Spring Symposium Series 1996: Adaptation, Co-evolution and Learning in Multiagent Systems
March 25-27, 1996
Stanford University, California
Coordination of multiple agents is essential for the viability of
systems in which these agents share resources. 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. This symposium focuses on
research that will address unique requirements for agents learning and
adapting to work with other agents. Among others, the symposia will
address the following issues:
- 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.
- Co-evolving multiple agents with similar/opposing interests.
- Inter-disciplinary research from fields like organizational theory,
game theory, psychology, sociology, economics, etc.
In addition to presentation of selected papers, the symposia will consist of
panel discussions, breakout groups, and invited talks. We will distribute
accepted papers and key discussion topics ahead of the symposium.
Papers to be presented
- Neeraj Arora and Sandip Sen, "Resolving Social Dilemmas Using Genetic
- H.H. Bui and D. Kieronska, "Negotiating agents that learn about other's
- Lawrence Bull and Terence C. Fogarty, "Evolution in cooperative
- Innes A. Ferguson and Grigoris J. Karakoulas, "Multiagent Learning and
Adaptation in an Information Filtering Market."
- Andrew Garland and Richard Alterman, "Multiagent Learning through
- Dan L. Grecu and David C. Brown, "Learning to Design Together."
- John Grefenstette and Robert Daley, "Methods for competitive and
- Salima Hassas and Jacques Bonneville, "Towards a self-organizational
approach for a parallel computation in a distributed production rule based
- Thomas Haynes, Kit Lau, and Sandip Sen, "Learning Cases to Compliment
Rules in Conflict Resolution."
- Owen Holland, "Multiagent systems: lessons from social insects and
- Hitoshi Matsubara, Itsuki Noda, and Kazuo Hiraki, "Learning of
Cooperative actions in multiagent systems: a case study of pass play in
- MV Nagendra Prasad and Victor Lesser, "Learning Situation-specific
Coordination in Generalized Partial Global Planning."
- MV Nagendra Prasad, Victor Lesser, and Susan Lander, "Learning
Organizational Roles in a Heterogeneous Multiagent System."
- Martha E. Pollack and Cristina Bichhieri, "The potential for the
evolution of cooperation among Web agents."
- Shounak Roychowdhury, Neeraj Arora, and Sandip Sen, "Effects of local
information on group behavior."
- Jurgen Schmidhuber, "Theory of multi-agent learning in unrestricted
- Peter Stone and Manuela Veloso, "Learning: A Case Study in Robotic
- Milind Tambe, Lewis Johnson, and Wei-Min Shen, "Adaptive Agent Tracking
in Real-world Multiagent domains: A Preliminary Report."
- Dajun Zeng and Katia Sycara, "Bayesian Learning in Negotiation."
Sandip Sen (Chair), University of Tulsa, email@example.com;
Devika Subramanian, Rice University; Jeff Rosenschein, The Hebrew
University; John J. Grefenstette, Naval Research Laboratory; Michael N. Huhns,
University of South Carolina; Tad Hogg, Xerox PARC