45 research outputs found
Translation-based Constraint Answer Set Solving
We solve constraint satisfaction problems through translation to answer set
programming (ASP). Our reformulations have the property that unit-propagation
in the ASP solver achieves well defined local consistency properties like arc,
bound and range consistency. Experiments demonstrate the computational value of
this approach.Comment: Self-archived version for IJCAI'11 Best Paper Track submissio
Specifying and Exploiting Non-Monotonic Domain-Specific Declarative Heuristics in Answer Set Programming
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
Constraints, Lazy Constraints, or Propagators in ASP Solving: An Empirical Analysis
Answer Set Programming (ASP) is a well-established declarative paradigm. One
of the successes of ASP is the availability of efficient systems.
State-of-the-art systems are based on the ground+solve approach. In some
applications this approach is infeasible because the grounding of one or few
constraints is expensive. In this paper, we systematically compare alternative
strategies to avoid the instantiation of problematic constraints, that are
based on custom extensions of the solver. Results on real and synthetic
benchmarks highlight some strengths and weaknesses of the different strategies.
(Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)Comment: Paper presented at the 33nd International Conference on Logic
Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1,
2017. 16 page
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