Abstract

This paper describes a method for learning flexible concepts. by which are meant concepts that lack precise definition and are contextqlependent. To describe such concepts, the method employs a two-tiered represen- tation. in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In e proposed method. the first tier, called Base Concept Representation (BCR), is created in two phases. In phase 1, the AQ-15 rule learning program is applied to induce a complete and consistent concept description from supplied examples. In phase 2, this description is optimized according to a domain-dependent quality criterion. The second tier, called the inferential concept interpretation dCI). consists of a procedure for flexible matching, and a set of inference rules. The proposed method has been implemented in the POSEIDON system. and experimentally tested on two real-world problems: [earning the concept of an acceptable umon contract, and learning voting patterns of Republicans and Democrats in the U.S. Congress. For comparison, a few other learning methods were also applied to the same problems. These methods included simple variants of exemplar-based learning, and an ID-3-tyl: decision tree learning, implemented m the ASSISTANT program. In the exl:riments, POSEIDON generated concept descriptions that were both, more accurate and also substantially simpler than those produced by the other methods.

Citations