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JACIII Vol.13 No.6 pp. 649-657
doi: 10.20965/jaciii.2009.p0649
(2009)

Paper:

Information Theoretic Approach for Measuring Interaction in Multiagent Domain

Sachiyo Arai and Yoshihisa Ishigaki

Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba

Received:
April 15, 2009
Accepted:
August 4, 2009
Published:
November 20, 2009
Keywords:
reinforcement learning, cooperative behavior, multi agent system
Abstract

Although a large number of reinforcement learning algorithms have been proposed for the generation of cooperative behaviors, the question of how to evaluate mutual benefit or loss among them is still open. As far as we know, an emerged behavior is regarded as a cooperative behavior when embedded agents have finally achieved their global goal, regardless of whether or not mutual interference has had any effect during the course of the learning process of each agent. Thus, we cannot detect any harmful interaction on the way to achieving a fully-converged policy. In this paper, we propose a measure based on information theory for evaluating the degree of interaction during the learning process from the viewpoint of information sharing. In order to discuss the bad effects of concurrent learning, we apply our proposed measure to a situation in which there exist conflicts among the agents, and we show the availability of our measure.

Cite this article as:
Sachiyo Arai and Yoshihisa Ishigaki, “Information Theoretic Approach for Measuring Interaction in Multiagent Domain,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.6, pp. 649-657, 2009.
Data files:
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