Exploiting Partial Knowledge in Declarative Domain-Specific Heuristics for ASP
This work addresses a bottleneck in ASP solving for combinatorial optimization, but it is incremental as it builds on existing heuristic techniques.
The authors tackled the problem of efficiently solving combinatorial problems in Answer Set Programming (ASP) by proposing a novel semantics for declarative domain-specific heuristics that exploit partial solver states, and experiments showed this combination can be vital for industrial-size problems.
Domain-specific heuristics are an important technique for solving combinatorial problems efficiently. We propose a novel semantics for declarative specifications of domain-specific heuristics in Answer Set Programming (ASP). Decision procedures that are based on a partial solution are a frequent ingredient of existing domain-specific heuristics, e.g., for placing an item that has not been placed yet in bin packing. Therefore, in our novel semantics negation as failure and aggregates in heuristic conditions are evaluated on a partial solver state. State-of-the-art solvers do not allow such a declarative specification. Our implementation in the lazy-grounding ASP system Alpha supports heuristic directives under this semantics. By that, we also provide the first implementation for incorporating declaratively specified domain-specific heuristics in a lazy-grounding setting. Experiments confirm that the combination of ASP solving with lazy grounding and our novel heuristics can be a vital ingredient for solving industrial-size problems.