CLIP : Concept Learning from Inference Pattern (2) : Concept Hierarchy Formation

Abstract

We view a set of chunks, the use of which makes inference more efficient, as a concept. The idea is based on the assumption that a chunk that appears often in an inference may mean something important. The extraction of a chunk is solely based on finding the repetition of a typical inference pattern in a given environment. This idea, implemented as CLIP (Concept Learning from Inference Pattern), adapts Genetic Algorithm like parallel search algorithm and when applied to the digraphs of a carry chain circuit, CLIP extracted the chunks corresponding to analog NOR and NOT. This paper discusses some of the important factors for concept hierarchy formation. Introduction of approximation is very important to step up to a more abstract level concept. This can also be processed as a reduction of digraph. Another important factor for the concept hierarchy formation is the characteristics of the inference system. This must be reflected on the matching cost. The different weight for the matching cost generates a hierarchy of different levels/depths. Environment of the inference system is also important. It must be reflected on the choice of color. Choice of a different color forms a hierarchy of different kinds. Presence of noise effects the performance, but the analysis indicates that CLIP can cope with a certain type of noise.