類型パターンの抽出に基づく帰納的学習と演繹的学習の統合

概要

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We describe a new learning method, CLiP (Concept Learning from Typical Patterns), that performs induction over colored directed graphs. CLiP is capable of performing both inductive and deductive learning by mapping the problems into a colored digraph representation. In contrast to earlier approaches, CLiP uses a single learning algorithm to solve both kinds of problems. The learning procedure can be characterized as a variation of beam search guided by a simple, but effective, heuristic:typical pattern heuristic. We demonstrate the applicability of CLiP to the tasks of (1) inductive learning for classification and (2) deductive learning for efficient problem-solving. We show that the performance of CLiP on these tasks is comparable to that of standard approaches. Our preliminary results suggest that the generality of CLiP can be attributed to the expressiveness of the colored digraph representation which allows a number of seemingly different learning problems to be solved by a single algorithm. The other functions of CLiP and the limitations are also discussed together with the related work.
社団法人人工知能学会の論文
1995-01-01