Combining individual and cooperative learning for multi-agent negotiations

  • Authors:

  • Leen-kiat Soh;Juan Luo

  • Affiliations:

  • University of Nebraska-Lincoln, Lincoln, NE;University of Nebraska-Lincoln, Lincoln, NE

  • Venue:
  • AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
  • Year:
  • 2003

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Abstract

In this paper, we propose a distributed multi-strategy learning methodology based on case-based reasoning in which an agent conducts both individual learning by observing its environment and cooperative learning by interacting with its neighbors. Cooperative learning is generally more expensive than individual learning due to the communication and processing overhead. Thus, our methodology employs a cautious utility-based adaptive mechanism to combine the two, an interaction protocol for soliciting and exchanging information, and the idea of a chronological casebase. Here we report on experimental results on the roles and effects of the methodology in a multiagent environment.