Translating Case-Based Reasoning into Abductive Logic Programming

This paper presents a translation method of casebased reasoning which changes similarity according to context into abductive logic programming based on a generalized stable model semantics [5]. This kind of dynamic similarity can be found in CBR systems for legal reasoning such as HYPO [1]. Abductive logic programming is suitable to implement this dynamic similarity by regarding abducible predicates as similarity and changing abducible predicates by context. In this paper, we de ne a relevance criteria of cases for defendant and plainti and show how to change similarity between cases according to its context (defendant vs plainti , the current case and the cited case). We show correspondence between properties in a case used for similarity and abducibles used in a translated abductive logic program and show that we can construct an argument by using abducibles which explains why the current case is similar to the cited case and the current case is not similar to every counter case.