Demand-Driven Discovery of Adaptation Knowledge

Abstract

A case-based approach to adaptation for estimation tasks is presented in which there is no requirement for explicit adaptation knowledge. Instead, a target case is estimated from the values of three existing cases, one retrieved for its similarity to the target case and the others to provide the knowledge required to adapt the similar case. With recursive application of the adaptation process, any problem space can be fully covered by fewer than nk selected cases, where n is the number of case attributes and k is the number of values of each attribute. Moreover, a k × k problem space is fully covered by any set of 2k - 1 known cases provided there is no redundancy in the case library. Circumstances in which the approach is appropriate are identified by theoretical analysis and confirmed by experimental results.