Name
Abstract | ||
|---|---|---|
Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages like Answer Set Programming (ASP) and FO(.) and a CSP modeling language such as Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory. An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justifications for the formulas of the non-grounded part, the justifications provide a recipe to construct a complete assignment that satisfies the non-grounded part. When a justification for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justified. Experimental results illustrate the power and generality of this approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1613/jair.4591 | JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH |
DocType | Volume | ISSN |
Journal | 52 | 1076-9757 |
Citations | PageRank | References |
10 | 0.62 | 46 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Broes De Cat | 1 | 64 | 6.24 |
| Marc Denecker | 2 | 1626 | 106.40 |
| Peter J. Stuckey | 3 | 4368 | 457.58 |
| Maurice Bruynooghe | 4 | 2767 | 226.05 |