Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System

摘要

This paper describes a method for learning flexible concepts, by which are meant concepts that lack precise definition and are context-dependent. To describe such concepts, the method employs a two-tiered representation, in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In the 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 (ICI), 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: learning the concept of an acceptable union 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-type decision tree learning, implemented in the ASSISTANT program. In the experiments, POSEIDON generated concept descriptions that were both, more accurate and also substantially simpler than those produced by the other methods.