Abstract
This paper presents the emergence of the cooperative behavior for the multiple agents by means of Genetic Programming (GP). Our experimental domain is the Tile World, a multi-agent test bed [Pollack90]. The world consists of a simulated robot agent and a simulated environment which is both dynamic and unpredictable. For the purpose of evolving the cooperative behavior, we propose three types of strategies, i.e, homogeneous breeding, heterogeneous breeding, and co-evolutionary breeding. The effectiveness of these three types of GP-based multi-agent learning is discussed with comparative experiments.
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© 1996 Springer-Verlag Berlin Heidelberg
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Iba, H. (1996). Emergent cooperation for multiple agents using genetic programming. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_967
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DOI: https://doi.org/10.1007/3-540-61723-X_967
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Online ISBN: 978-3-540-70668-7
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