2007•Unpublished venueRequires access

Incremental answer set programming : a preliminary report

Mona Gharib, Torsten H. Schaub, Robert E. Mercer

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Abstract

Abstract. An alternative approach to answer set programming is developed in order to remedy the problems of “relevance ” and “modularity ” of answer set semantics for logic programs. The fundamental idea is to re-introduce monotonicity. In particular, we introduce the alternative concept of ι-answer sets, which are characterized by their applied rules. Furthermore, we develop a sound and complete theorem proving method for our incremental approach to answer set programming, which allows for query-oriented computations. Our proof procedure can manage negation in logic programming. Moreover, it enables us to deal with variables using unification instead of grounding. 1

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What this paper is about

Abstract. An alternative approach to answer set programming is developed in order to remedy the problems of “relevance ” and “modularity ” of answer set semantics for logic programs. The fundamental idea is to re-introduce monotonicity. In particular, we introduce the alternative concept of ι-answer sets, which are characterized by their applied rules. Furthermore, we develop a sound and complete theorem proving method for our incremental approach to answer set programming, which allows for query-oriented computations. Our proof procedure can manage negation in logic programming. Moreover, it enables us to deal with variables using unification instead of grounding. 1

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Available abstract

Abstract. An alternative approach to answer set programming is developed in order to remedy the problems of “relevance ” and “modularity ” of answer set semantics for logic programs. The fundamental idea is to re-introduce monotonicity. In particular, we introduce the alternative concept of ι-answer sets, which are characterized by their applied rules. Furthermore, we develop a sound and complete theorem proving method for our incremental approach to answer set programming, which allows for query-oriented computations. Our proof procedure can manage negation in logic programming. Moreover, it enables us to deal with variables using unification instead of grounding. 1

Key concepts: Answer set programming, Stable model semantics, Logic programming, Unification, Computer science, Programming language, Set (abstract data type), Semantics (computer science)

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