Detecting Discontinuities in Case-Bases

No. 1: Agents, AI in Art and Entertainment, Knowledge Representation, and Learning

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 13

Case-based Reasoning

This paper describes a discontinuity detection method for case-bases and data bases. A discontinuous case or data record is defined as a case or data record whose specific attribute values are very different from those of other records retrieved with identical or similar input specifications. Using the proposed method, when a user gives an input specification, he/she can retrieve not only exactly-matched cases, but also similar cases and discontinuous cases. The proposed method has three steps: (1) Retrieving case records with input specifications which are the same as or similar to a user’s input specification (Mcaybe Similar Case, MSC), (2) Selecting a case record which most closely matches the user’s input specification among MSCs (Base Case, BC), and (3) Detecting cases among MSCs whose output specifications are very different from those of BC. The proposed method has been implemented in the CARET case-based retrieval tool operating on commercial RDBMS. Because case-based reasoning systems rely on the underlying assumption that similar input specifications retrieve similar case records, discontinuity detection in case-bases is indispensable, and our proposed method is especially useful.