著者

山口 智浩 野村 勇治 田中 康祐 谷内田 正彦

出版者

一般社団法人 人工知能学会

雑誌

人工知能 (ISSN:21882266)

巻号頁・発行日

vol.12, no.6, pp.870-880, 1997-11-01 (Released:2020-09-29)

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The advantage of emergence is that various solutions are emerged. However, it takes large computation cost to emerge them since it requires the numbers of iterations of simulation. So we try to reduces the computation cost without losing variety of solutions by introducing the abstraction technique in Artificial Intelligence. This paper presents Isomorphism Based Reinforcement Learning by Isomorphism of Actions that reduces the learning cost without losing variety of solutions. Isomorphism is one of the concepts in Enumerative Combinatorics of mathematics. First we explain Isomorphism of Actions, then explain Isomorphism of Behaviors. Isomorphic behaviors those perform the same task can be obtained by transforming the learning result of the task by "the appropriate permutation". However, a priori knowledge that represents "the appropriate permutation" is not always given, so this paper uses the generate & test method that first generates the isomorphic learning results by transforming the learning result of reinforcement learning for a task by the combinatorial permutations, then tests to select two kinds of the behaviors performing the following tasks ; (1) isomorphic behaviors those perform the same task ; (2) discovery of the behaviors those are converged to the new task state. Since the acquired learning results are isomorphic each other, the merits of our method are those the time cost for generating various learning results is small and also the space cost is small too because it needs only the original learning result and the set of permutations for it. For these reasons, this method is significant for realizing the learning various behaviors for the dynamic environment or multiagent.