Multi Agent Systems with Symbiotic Learning and Evolution using GNP.

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  • Eguchi Toru
    Graduate School of Information Science and Electrical Engineering, Kyusyu University
  • Hirasawa Kotaro
    Graduate School of Information, Production and Systems, Waseda Univercity
  • Hu Jinglu
    Graduate School of Information Science and Electrical Engineering, Kyusyu University
  • Murata Junichi
    Graduate School of Information Science and Electrical Engineering, Kyusyu University

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  • Ｇｅｎｅｔｉｃ　Ｎｅｔｗｏｒｋ　Ｐｒｏｇｒａｍｍｉｎｇを用いた共生学習進化型マルチエージェントシステム
  • Genetic Network Programming オ モチイタ キョウセイ ガクシュウ シンカガタ マルチエージェント システム

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Abstract

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

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