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

TL;DR

This paper develops meta-interpreters in Answer Set Programming to implement various preference handling semantics, demonstrating the flexibility and efficiency of ASP and DLV for prototyping and experimenting with advanced logic programming features.

Contribution

It introduces suitable meta-interpreters for multiple preference semantics in ASP using DLV, including an innovative approach for weakly preferred answer sets utilizing weak constraints.

Findings

Meta-interpreters effectively implement preference semantics in ASP.

DLV demonstrates efficiency in handling complex preference-based reasoning.

Meta-interpreters facilitate rapid prototyping and experimentation in knowledge representation.

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.