9 research outputs found
Introduction to the 28th International Conference on Logic Programming Special Issue
We are proud to introduce this special issue of the Journal of Theory and
Practice of Logic Programming (TPLP), dedicated to the full papers accepted for
the 28th International Conference on Logic Programming (ICLP). The ICLP
meetings started in Marseille in 1982 and since then constitute the main venue
for presenting and discussing work in the area of logic programming
Theory Solving Made Easy with Clingo 5
Answer Set Programming (ASP) is a model, ground, and solve paradigm. The integration of application- or theory-specific reasoning into ASP systems thus impacts on many if not all elements of its workflow, viz. input language, grounding, intermediate language, solving, and output format. We address this challenge with the fifth generation of the ASP system clingo and its grounding and solving components by equipping them with well-defined generic interfaces facilitating the manifold integration efforts. On the grounder\u27s side, we introduce a generic way of specifying language extensions and propose an intermediate format accommodating their ground representation. At the solver end, this is accompanied by high-level interfaces easing the integration of theory propagators dealing with these extensions
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
Boosting Answer Set Optimization with Weighted Comparator Networks
Answer set programming (ASP) is a paradigm for modeling knowledge intensive
domains and solving challenging reasoning problems. In ASP solving, a typical
strategy is to preprocess problem instances by rewriting complex rules into
simpler ones. Normalization is a rewriting process that removes extended rule
types altogether in favor of normal rules. Recently, such techniques led to
optimization rewriting in ASP, where the goal is to boost answer set
optimization by refactoring the optimization criteria of interest. In this
paper, we present a novel, general, and effective technique for optimization
rewriting based on comparator networks, which are specific kinds of circuits
for reordering the elements of vectors. The idea is to connect an ASP encoding
of a comparator network to the literals being optimized and to redistribute the
weights of these literals over the structure of the network. The encoding
captures information about the weight of an answer set in auxiliary atoms in a
structured way that is proven to yield exponential improvements during
branch-and-bound optimization on an infinite family of example programs. The
used comparator network can be tuned freely, e.g., to find the best size for a
given benchmark class. Experiments show accelerated optimization performance on
several benchmark problems.Comment: 36 page
Answer Set Solving with Lazy Nogood Generation
Although Answer Set Programming (ASP) systems are highly optimised, their performance is sensitive to the size of problem encodings. We address this deficiency by introducing a new extension to ASP solving. The idea is to integrate external propagators to represent parts of the encoding implicitly, rather than generating it a-priori. To match the state-of-the-art in conflict-driven solving, however, external propagators can generate an encoding of their inference on demand. We demonstrate the applicability of our approach in a novel Constraint Answer Set Programming system that can seamlessly integrate Constraint Programming techniques without sacrificing the advantages of conflict-driven techniques. Experiments provide evidence for computational impact
Conflict-Driven Constraint Answer Set Solving with Lazy Nogood Generation
We present a new approach to enhancing answer set programming (ASP) with constraint programming (CP) techniques based on conflict-driven learning and lazy nogood generation