  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.
