経験則と分類子システムを併用した知識ベースの動的洗練 : 工程スケジューリングへの適用と評価(知識獲得)

概要

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This paper presents a new method for dynamic knowledge base refinement by integrating both heuristic and GA-based Learning Classifier System LCS techniques and describes its application to a manufacturing scheduling problem for automated transportation systems. The key idea of the proposed method is that the condition part of a rule should be divided into two parts: indispensable conditions and discriminate conditions. The formers are generated by a diagnosing type knowledge-based system. The latters and action parts are generated and refined by genetic algorithms. Using this method, we can easily input initial rule sets, refine the rules without generating unapplicable ones, and reduce the computation time for learning. The pre-requisites of the proposed method are so general that the method can be widely applied to the rule refinement tasks in various kinds of rule-based systems.
社団法人日本オペレーションズ・リサーチ学会の論文
1994-10-11