The Classification, Detection and Handling of Imperfect Theory Problems

Abstract

In recent years knowledge-based techniques like explanation-based learning, qualitative reasoning and case-based reasoning have been gaining considerable popularity in AI. Such knowledge-based methods face two difficult problems: 1) the performance of the system is fundamentally limited by the knowledge initially encoded into its domain theory 2) the encoding of just the right knowledge to enable the system to function properly over a wide range of tasks and situations is virtually impossible for a complex domain. This paper describes research directed towards the construction of a system that will detect and correct problems with domain theories. This will enable knowledge-based systems to operate with imperfect domain theories and automatically correct the imperfections whenever they pose problems. This paper discusses the classification of imperfect theory problems, strategies for their detection and an approach based on experiment design to handle different types of imperfect theory problems.