Inductive Learning of Declarative Domain-Specific Heuristics for ASP

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.

Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming

Domain-specific heuristics are an essential technique for solving combinatorial problems efficiently. Current approaches to integrate domain-specific heuristics with Answer Set Programming (ASP) are unsatisfactory when dealing with heuristics that are specified non-monotonically on the basis of partial assignments. Such heuristics frequently occur in practice, for example, when picking an item that has not yet been placed in bin packing. Therefore, we present novel syntax and semantics for declarative specifications of domain-specific heuristics in ASP. Our approach supports heuristic statements that depend on the partial assignment maintained during solving, which has not been possible before. We provide an implementation in ALPHA that makes ALPHA the first lazy-grounding ASP system to support declaratively specified domain-specific heuristics. Two practical example domains are used to demonstrate the benefits of our proposal. Additionally, we use our approach to implement informed} search with A*, which is tackled within ASP for the first time. A* is applied to two further search problems. The experiments confirm that combining lazy-grounding ASP solving and our novel heuristics can be vital for solving industrial-size problems.

Applying Incremental Answer Set Solving to Product Configuration

In this paper, we apply incremental answer set solving to product configuration. Incremental answer set solving is a step-wise incremental approach to Answer Set Programming (ASP). We demonstrate how to use this technique to solve product configurations problems incrementally. Every step of the incremental solving process corresponds to a predefined configuration action. Using complex domain-specific configuration actions makes it possible to tightly control the level of non-determinism and performance of the solving process. We show applications of this technique for reasoning about product configuration, like simulating the behavior of a deterministic configuration algorithm and describing user actions.