Abstract
Answer Set Programming (ASP) is a powerful formalism for knowledge representation and common sense reasoning, particularly suitable for representing incomplete knowledge and nonmonotonic reasoning. ASP is used in Artificial Intelligence applications such as diagnosis and planning. Aggregate functions are an important extension to ASP that allow the representation of concepts involving sets of data in a natural way. We have analyzed the properties of recursive aggregates, identified rich classes of programs with a unique answer set, and defined a new notion of unfounded set that we used in answer set computations. Experimental results on the implemented prototype, obtained by extending DLV, show a significant performance improvement of our enhanced approach over existing techniques. In this paper we summarize some of our results, which are fully described in the thesis of the same title.