LOAIPLAug 19, 2016

lpopt: A Rule Optimization Tool for Answer Set Programming

arXiv:1608.05675v227 citations
Originality Incremental advance
AI Analysis

This tool addresses efficiency issues for ASP users by automating rule optimization, reducing the need for expert hand-tuning, though it is incremental as it builds on prior work.

The authors tackled the problem of large grounding sizes in answer set programming (ASP) by developing lpopt, a tool that decomposes large rules into smaller ones, resulting in improved solving performance as shown in experimental evaluation.

State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant hand-tuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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