帰納学習における帰納論理プログラミングと遺伝的プログラミングの統合

概要

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Two approaches to inducing a concept represented in first order logic are inductive logic programming (ILP) and genetic programming (GP). In ILP, concept learning can be considered as a search in the space specified by the background knowledge, and in which the goal concept is represented by Horn clauses. On the other hand, in GP, the search space is specified by terminal and nonterminal symbols, and the goal is represented generally by S-expressions. These two approaches are very similar in terms of their methods and goals, yet their combination in previous work is rare. In this paper, we propose a method that synthesizes the inductive logic programming and genetic programming approaches. The concept behind this approach is to combine the search method of GP, that is, Genetic Algorithm, with the type and mode methods of ILP. We have implemented a system called SYNGIP (SYNthesized system with Genetic programming and Inductive logic Programming) based on the method. Experimental results show that the proposed method can be used to treat, in the same way, learning from training examples that do not have discrete classes, and learning from both positive and negative training examples. Moreover, the proposed method constitutes a novel solution to the c1osure problem and provides a new bias for concept learning.
社団法人人工知能学会の論文
1999-03-01