Inductive Learning of Declarative Domain-Specific Heuristics for ASP

TL;DR

This paper introduces a novel ILP-based method for automatically learning declarative heuristics in ASP, improving solver performance on complex problems without manual heuristic design.

Contribution

It presents the first approach to automatically induce domain-specific heuristics for ASP using inductive logic programming from example answer sets.

Findings

Learned heuristics enhance ASP solving efficiency on larger instances.

Automatically generated heuristics outperform manually crafted ones in some cases.

The approach reduces the need for expert knowledge in heuristic development.

Abstract

Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific heuristics to improve solving performance. However, such heuristics must be invented manually so far. Inventing domain-specific heuristics for answer-set programs requires expertise with the domain under consideration and familiarity with ASP syntax, semantics, and solving technology. The process of inventing useful heuristics would highly profit from automatic support. This paper presents a novel approach to the automatic learning of such heuristics. We use Inductive Logic Programming (ILP) to learn declarative domain-specific heuristics from examples stemming from (near-)optimal answer sets of small but representative problem instances. Our experimental results indicate that the learned heuristics can improve solving performance and solution quality when solving larger, harder instances of the same problem.