8 research outputs found
Extending the Finite Domain Solver of GNU Prolog
International audienceThis paper describes three significant extensions for the Finite Domain solver of GNU Prolog. First, the solver now supports negative integers. Second, the solver detects and prevents integer overflows from occurring. Third, the internal representation of sparse domains has been redesigned to overcome its current limitations. The preliminary performance evaluation shows a limited slowdown factor with respect to the initial solver. This factor is widely counterbalanced by the new possibilities and the robustness of the solver. Furthermore these results are preliminary and we propose some directions to limit this overhead
Lazy Model Expansion: Interleaving Grounding with Search
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 ASP,
FO(.) and 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 blowup of the size of the theory caused by the
reduction 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.
The theoretical framework captures existing approaches for tackling the
grounding bottleneck such as lazy clause generation and grounding-on-the-fly,
and presents a generalization of the 2-watched literal scheme. We present an
algorithm for lazy model expansion and integrate it in a model generator for
FO(ID), a language extending first-order logic with inductive definitions. The
algorithm is implemented as part of the state-of-the-art FO(ID) Knowledge-Base
System IDP. Experimental results illustrate the power and generality of the
approach
Compiling input∗ FO(·) inductive definitions into tabled Prolog rules for IDP3
FO(·)IDP3 extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and uses XSB Prolog to compute their interpretation. The interpretation of these predicates is then added to the input structure and their definitions are removed from the theory; the further processing is then done by the standard IDP3 system. Experimental results show the effectiveness of our method.status: publishe