An adaptation heuristic for case-based estimation

Abstract

Paradoxically, the knowledge acquisition effort associated with rule-based approaches to case adaptation is precisely the overhead that CBR aims to reduce. An adaptation heuristic for case-based estimation is presented which does not rely on domain-specific rules. The approach has been implemented in a case-based reasoner called CREST (Case-based Reasoning for ESTimation) in which the concept of case dominance plays an important role in checking estimates based on the adaptation heuristic and in the maintenance of consistency in the case library. Circumstances in which the adaptation heuristic is appropriate are identified by theoretical analysis and confirmed by experimental results. It is shown to give best results when the value of a case is an additive function of its attributes. The use of domain knowledge to guide the estimation process is examined as a means of enabling the case-based reasoner to cope with departures from this assumption caused by interaction between case attributes.