Learning Abductive and Nonmonotonic Logic Programs

Abstract

We investigate the integration of induction and abduction in the context of logic programming. Our integration proceeds in a way that we learn theories for abductive logic programming (ALP) in the framework of inductive logic programming (ILP). Both ILP and ALP are important research areas in logic programming and AI. ILP provides theoretical frameworks and practical algorithms for inductive learning of relational descriptions in the form of logic programs (Muggleton, 1992; Lavrač and Džeroski, 1994; De Raedt, 1996). ALP, on the other hand, is usually considered as an extension of logic programming to deal with abduction so that incomplete information is represented and handled easily (Kakas et al., 1992). Learning abductive programs has also been proposed as an extension of previous work on ILP (Dimopoulos and Kakas, 1996b; Kakas and Riguzzi, 1997).1 The important question here is “how do we learn abductive theories?”