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Learning other agents' preferences in multi-agent negotiation using the Bayesian classifier

journal contribution
posted on 1999-12-01, 00:00 authored by H H Bui, Svetha VenkateshSvetha Venkatesh, D Kieronska
In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

History

Journal

International Journal of Cooperative Information Systems

Volume

8

Issue

4

Pagination

275 - 293

Publisher

World Scientific Publishing

Location

Singapore, Republic of Singapore

ISSN

0218-8430

Language

eng

Publication classification

CN.1 Other journal article

Copyright notice

1999, World Scientific Publishing