Nonmonotonic Inductive Logic Programming (invited talk)

Chiaki Sakama

Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'01), Lecture Notes in Artificial Intelligence 2173, pages 62-80, Springer-Verlag, 2001.

Abstract

Nonmonotonic logic programming (NMLP) and inductive logic programming (ILP) are two important extensions of logic programming. The former aims at representing incomplete knowledge and reasoning with commonsense, while the latter targets the problem of inductive construction of a general theory from examples and background knowledge. NMLP and ILP thus have seemingly different motivations and goals, but they have much in common in the background of problems, and techniques developed in each field are related to one another. %both handle nonmonotonic problems and perform hypothetical reasoning in %incomplete knowledge bases. This paper presents techniques for combining these two fields of logic programming in the context of nonmonotonic inductive logic programming (NMILP). We review recent results and problems to realize NMILP.

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