Approximation of Utility Functions by Learning Similarity Measures

Abstract

Expert systems are often considered to be logical systems producing outputs that can only be correct or incorrect. However, in many application domains results cannot simply be distinguished in this restrictive form. Instead to classify a result as correct or incorrect, here results might be more or less useful for solving a given problem or for satisfying given user demands, respectively. In such a situation, an expert system should be able to estimate the utility of possible outputs a-priori in order to produce reasonable results. In Case-Based Reasoning this is done by using similarity measures which can be seen as an approximation of the domain specific, but a-priori unknown utility function. In this article we present an approach how this approximation of utility functions can be facilitated by employing machine learning techniques.