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

Abstract

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.

References in corpus (5)

• The DLV System for Knowledge Representation and Reasoning
• The third open Answer Set Programming competition
• Computing Stable Models of Normal Logic Programs Without Grounding
• ASPeRiX, a First Order Forward Chaining Approach for Answer Set Computing
• Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP

Cited by in corpus (2)

• Finite-Choice Logic Programming
• Inductive Learning of Declarative Domain-Specific Heuristics for ASP