Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Strategic Coalition for Improving Generalization Ability of Multi-agent with Evolutionary Learning

진화학습을 이용한 다중에이전트의 일반화 성능향상을 위한 전략적 연합

  • Published : 2004.02.01
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Abstract

In dynamic systems, such as social and economic systems, complex interactions emerge among its members. In that case, their behaviors become adaptive according to Changing environment. In many cases, an individual's behaviors can be modeled by a stimulus-response system in a dynamic environment. In this paper, we use the Iterated Prisoner's Dilemma (IPD) game, which is simple yet capable of dealing with complex problems, to model the dynamic systems. We propose strategic coalition consisting of many agents and simulate their emergence in a co-evolutionary learning environment. Also we introduce the concept of confidence for agents in a coalition and show how such confidences help to improve the generalization ability of the whole coalition. Experimental results are presented to demonstrate that co-evolutionary learning with coalitions and confidence allows better performing strategies that generalize well.

사회시스템이나 경제시스템 같이 동적으로 변하는 시스템에서는 그 구성원들 간에 복잡한 상호작용(행동)이 나타나게 되는데 구성원들의 행동은 변화하는 환경에 따라 적응하는 경향을 보인다. 그리고 이들의 행동양상은 흔히 생물학 분야의 조건반사에 비유되기도 한다. 본 논문에서는 복잡한 사회 현상을 모델링하고 분석하기 위하여 반복적 죄수의 딜레마 게임 상에서 에이전트들의 전략적 연합을 통하여 변화하는 환경에 잘 적응하는 일반화 능력이 우수한 에이전트들을 자동 생성하는 방법을 제안한다. 또한 에이전트에 신뢰도를 부여하여 연하의 의사결정에 참가하게 함으로써 일반화 성능을 향상시키는 방법을 소개한다 실험결과, 전략적 연합을 이용하여 진화된 에이전트들은 테스트 에이전트들에 비하여 일반화 성능이 우수함을 확인할 수 있었다.

Keywords

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