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.

翻译：领域特定启发式是高效求解大规模或计算难题的关键技术。答案集编程（ASP）系统支持声明式规范的领域特定启发式，以提升求解性能。然而，迄今为止此类启发式必须由人工设计。为答案集程序设计领域特定启发式需要具备相关领域的专业知识，并熟悉ASP语法、语义及求解技术。设计有用启发式的过程若能获得自动化支持将大有裨益。本文提出了一种自动学习此类启发式的新方法。我们采用归纳逻辑编程（ILP）技术，从源自小型但具有代表性的问题实例的（近）最优答案集示例中，学习声明式领域特定启发式。实验结果表明，所学得的启发式在求解同一问题的更大规模、更困难实例时，能够改善求解性能与解的质量。