Learning with extended logic programs

E. Lamma, L. M. Pereira, and F. Riguzzi. In A. G. Cohn, L. K. Shubert, and S. C. Shapiro, editors, Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR'98), Trento, Italy, pages 99--108. Morgan Kaufmann, June 1998. More behind this link.

Abstract

We discuss the adoption of a three-valued setting for inductive concept learning. Distinguishing between what is true, what is false and what is unknown can be useful in situations where decisions have to be taken on the basis of scarce information. In a three-valued setting, we want to learn a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. To this purpose, we adopt extended logic programs under a well-founded semantics as the representation formalism for learning. In this way, we are able to represent both the concept and its opposite and deal with incomplete or unknown information. We discuss various approaches to be adopted in order to handle possible inconsistencies. Default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions to a positive concept are identified from negative examples, whereas exceptions to a negative concept are identified from positive examples. Exceptions can be generalized, in their turn, by learning within a hierarchy of defaults.

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E Lamma, elamma@deis.unibo.it,

L M Pereira, lmp@di.fct.unl.pt,