推論過程からの概念学習(1) : 類型的推論過程の抽出

抄録

A new concept learning method CLIP (Concept Learning from Inference Pattern) is proposed. CLIP learns new concepts from inference patterns in contrast with the fact that most of the conventional concept learning methods learn a new concept from positive/negative examples. The learned concepts enables efficient inference on a more abstract level. The learning process consists of the following two steps : 1) Convert the original inference patterns to a colored digraph, and 2) Extract a set of typical patterns which appears frequently in the digraph. The basic idea is that : 1) The smaller the size of the digraph becomes, the less becomes the number of the data to handle and accordingly the more efficient becomes the inference that uses these data, 2) The reduced graph does not lose information, and the original information can be restored whenever needed, and 3) The reduced node represents a new cencept component (a new vocabulary). A parallel-search algorithm based on "Pattern Modification" (mu-tation : to find a typical pattern), "Pattern Combination" (crossover : to mix patterns), and "View Selection" (to select a good set of patterns) extracts a set of typical patterns (chunks) which appear frequently in the digraph. The algorithm is similar to a previously reported Genetic Algorithm. In Pattern Modification, a partial digraph representing some meaningful component are extracted as a Pattern. In Pattern Combination, a new View is created as a set of Patterns. In View Selection, Views which result in smaller digraphs after being rewritten by the Patterns in the View are selected. Without this algorithm, we may have to rely on an exhaustive search which is computationally very expensive.