Computing Preferred Answer Sets by Meta-Interpretation in Answer Set Programming

Abstract

Most recently, Answer Set Programming (ASP) is attracting interest as a new paradigm for problem solving. An important aspect which needs to be supported is the handling of preferences between rules, for which several approaches have been presented. In this paper, we consider the problem of implementing preference handling approaches by means of meta-interpreters in Answer Set Programming. In particular, we consider the preferred answer set approaches by Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin. We present suitable meta-interpreters for these semantics using DLV, which is an efficient engine for ASP. Moreover, we also present a meta-interpreter for the weakly preferred answer set approach by Brewka and Eiter, which uses the weak constraint feature of DLV as a tool for expressing and solving an underlying optimization problem. We also consider advanced meta-interpreters, which make use of graph-based characterizations and often allow for more efficient computations. Our approach shows the suitability of ASP in general and of DLV in particular for fast prototyping. This can be fruitfully exploited for experimenting with new languages and knowledge-representation formalisms.

34 pages, appeared as a Technical Report at KBS of the Vienna University of Technology, see http://www.kr.tuwien.ac.at/research/reports/

References in corpus (3)

• Smodels: A System for Answer Set Programming
• Logic Programs with Compiled Preferences
• Answer Set Planning Under Action Costs

Cited by in corpus (5)

• The DLV System for Knowledge Representation and Reasoning
• A general approach to belief change in answer set programming
• Towards Automated Integration of Guess and Check Programs in Answer Set Programming: A Meta-Interpreter and Applications
• Controlled Natural Language Processing as Answer Set Programming: an Experiment
• A Family of Descriptive Approaches To Preferred Answer Sets