Combining Individual and Cooperative Learning for Multiagent Negotiations

Leen Kiat Soh, Juan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the Interantional Conference on Autonomous Agents
EditorsJ.S. Rosenschein, T. Sandholm, M. Wooldridge, M. Yakoo
Pages1122-1123
Number of pages2
Volume2
StatePublished - 2003
EventProceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03 - Melbourne, Vic.
Duration: Jul 14 2003Jul 18 2003

Other

OtherProceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 03
CityMelbourne, Vic.
Period7/14/037/18/03

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Keywords

  • Case-based reasoning
  • Cooperative learning
  • Distributed learning
  • Multiagent systems

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Soh, L. K., & Luo, J. (2003). Combining Individual and Cooperative Learning for Multiagent Negotiations. In J. S. Rosenschein, T. Sandholm, M. Wooldridge, & M. Yakoo (Eds.), Proceedings of the Interantional Conference on Autonomous Agents (Vol. 2, pp. 1122-1123)