Improving DPLL Solver Performance with Domain-Specific Heuristics: the ASP Case

TL;DR

This paper presents a framework for integrating domain-specific heuristics into DPLL-based ASP solvers, significantly enhancing their performance on challenging instances by learning heuristics offline from representative data.

Contribution

It introduces a novel off-line learning approach for domain-specific heuristics in DPLL solvers, tailored for Answer Set Programming, improving efficiency and consistency.

Findings

Performance improved by up to 3 orders of magnitude on hard instances

Nearly eliminated solver timeouts due to long wait times

Heuristics learned offline from representative instances

Abstract

In spite of the recent improvements in the performance of the solvers based on the DPLL procedure, it is still possible for the search algorithm to focus on the wrong areas of the search space, preventing the solver from returning a solution in an acceptable amount of time. This prospect is a real concern e.g. in an industrial setting, where users typically expect consistent performance. To overcome this problem, we propose a framework that allows learning and using domain-specific heuristics in solvers based on the DPLL procedure. The learning is done off-line, on representative instances from the target domain, and the learned heuristics are then used for choice-point selection. In this paper we focus on Answer Set Programming (ASP) solvers. In our experiments, the introduction of domain-specific heuristics improved performance on hard instances by up to 3 orders of magnitude (and 2 on average), nearly completely eliminating the cases in which the solver had to be terminated because the wait for an answer had become unacceptable.