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. 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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