Introspective reasoning for index refinement in case-based reasoning

Abstract

Introspective reasoning can enable a reasoner to learn by refining its own reasoning processes. In order to perform this learning, the system must monitor the course of its reasoning to detect learning opportunities and then apply appropriate learning strategies. This article describes lessons learned from research on a computer model of how introspective reasoning can guide failure-driven learning. The computer model monitors its own reasoning by comparing it to a model of the desired behaviour of its reasoning, and learns in response to deviations from the ideal defined by the model. The approach is applied to the problem of determining indices for selecting cases from a case-based planner's memory. Experiments show that learning driven by this introspective reasoning both decreases retrieval effort and improves the quality of plans retrieved, increasing the overall performance of the planning system compared to case learning alone.