A Learning Mechanism for Logic Programs Using Dynamically Shared Substructures

A reasoning method that proves a predicate logic formula by reducing its graph representation is proposed. Since the method directly reduces a logic formula represented by a graph, it can be understood to self-optimize a graph representation, meaning that it automatically transforms a logic formula into an eecient form equivalent to that acquired by Explanation-Based Learning. By sharing the original subgraphs between the learned formulae, reasoning eeciency does not deteriorate even after learning several examples. Therefore, the utility problem is overcome in the sense that no extra search is necessary for macros. The present paper demonstrates these facts in simple list manipulation problems and by proving geometric theories.

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