Selective learning of macro-operators with perfect causality

  • Authors:

  • Seiji Yamada;Saburo Tsuji

  • Affiliations:

  • Department of Control Engineering, Faculty of Engineering Science, Osaka University, Toyonaka, Japan;Department of Control Engineering, Faculty of Engineering Science, Osaka University, Toyonaka, Japan

  • Venue:
  • IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
  • Year:
  • 1989

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Abstract

A macro-operator is an integrated operator consisting of plural primitive operators and enables a problem solver to solve more efficiently. However, if a learning system generates and saves all macro-operators extracted from worked examples, they will increase explosively and eventually its problem solving will be less efficient than even a non-learning system. Thus, it is very important for macro-operator learning to select only the effective macro-operators. To cope with this problem, we propose a new method to select macrooperators by Perfect Causality, a new heuristic, and generalization of them with EBG. Both in classical robot planning and solving algebraic equations, we made the experiments using a selective macro-learning system with Perfect Causality, a non-selectively macrolearning system and a non-learning system. The experimental results verify much higher efficiency of the selective learning system than the other two systems over a lot of various problems. Finally, we discuss Perfect Causality as an operationality criterion in EBL perspective.