Constraint-Based Modeling of Case-Based Inference

Abstract

In this chapter, we adopt a constraint-based view of the CBI hypothesis, according to which the similarity of inputs imposes a constraint on the similarity of associated outcomes in the form of a lower bound. A related inference mechanism then allows for realizing CBI as a kind of constraint propagation. We also discuss representational issues and algorithms for putting the idea of learning within this framework into action. The chapter is organized as follows: Section 3.1 introduces the aforementioned formalization of the CBI hypothesis. A case-based inference scheme which emerges quite naturally from this formalization is proposed in Section 3.2 and further developed in Section 3.3. Case-based learning is discussed in Section 3.4. In Section 3.5, some applications of case-based inference in the context of statistics are outlined. The chapter concludes with a brief summary and some complementary remarks in Section 3.6.