ABSTRACT

This chapter shows that research in Logic Programming (LP), with its necessary extensions and improvements, can contribute a formal and efficient representation of knowledge that emerges from the recent research in Knowledge Representation (KR). It discusses some classical Artificial Intelligence (AI) paradigms and proposals to accommodate them into a logical framework. The chapter considers an important area of KR research, i.e. non-monotonic reasoning, which is producing a number of different non-standard logics to deal with common sense reasoning tasks, and is also playing a more and more important role in LP, especially for the treatment of negation. It reviews some of the more popular paradigms for representing knowledge which emerged from the AI community, namely production systems or rule based representations, associative representations such as semantic networks and object centred or frame based representations. The use of LP to solve KR problems is a very promising research direction and the cross-fertilization between the two fields can be very valuable.