Learning non-monotonic logic programs: Learning exceptions

In this paper we present a framework for learning non-monotonic logic programs. The method is parametric on a classical learning algorithm whose generated rules are to be understood as default rules. This means that these rules must be tolerant to the negative information by allowing for the possibility of exceptions. The same classical algorithm is then used to learn recursively these exceptions. We prove that the non-monotonic learning algorithm that realizes these ideas converges asymptotically to the concept to be learned. We also discuss various general issues concerning the problem of learning nonmonotonic theories in the proposed framework.