プロダクションルールの分解とその効果

概要

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This paper proposes a new learning mechanism called "production decomposition", which decomposes a single expensive production into several cheap ones. By the decomposition, instantiations of the expensive production are generated little by little. As a result, the number of instantiations that are actually calculated decreases in some cases improving the inference speed. Since the maximum number of instantiations during inference greatly decreases, the memory update cost of the state-saving-based pattern matchers such as Rete and Treat can also be reduced. There exists the optimal number of decomposites which minimizes the execution time. The decomposition, together with conventional composition, could optimize the size of individual production from the view point of inference speed. Related works, such as production composition, intermediate concept learning and the lazy matching algorithm are discussed to clarify the position of the proposed method.
社団法人人工知能学会の論文
1992-01-01