64,869 research outputs found
Implementing Non-Linear Constraints with Cooperative Solvers
We investigate the use of cooperation between solvers in the scheme of constraint logic programming languages over the domain of non-linear polynomial constraints. Instead of using a general and often inefficient decision procedure we propose a new approach for handling these constraints by cooperating specialised solvers. Our approach requires the design of a client/server architecture to enable communication between the various components. The main modules are a linear solver, a non-linear solver, a constraint manager, a communication protocol component and an answer processor module. This work is motivated by the need for a declarative system for robot motion planning and geometric problem solving. We have implemented a prototype called \groak %({\bf \sf C\raisebox{.2ex}o}nstraint {\bf \sf S}ystem {\bf \sf %A}r{\bf \sf \raisebox{.2ex}c}hitecture) (\textbf{\textsf C}\raisebox{.2ex}{\textbf{\textsfo}}nstraint \textbf{\textsfS}ystem \textbf{\textsfA}r\raisebox{.2ex}{\textbf{\textsfc}}hit- ecture) to validate our approach using cooperating solvers for non-linear constraints over the real numbers. Our language is illustrated by an example that also shows the advantages of cooperation
On Relation between Constraint Answer Set Programming and Satisfiability Modulo Theories
Constraint answer set programming is a promising research direction that
integrates answer set programming with constraint processing. It is often
informally related to the field of satisfiability modulo theories. Yet, the
exact formal link is obscured as the terminology and concepts used in these two
research areas differ. In this paper, we connect these two research areas by
uncovering the precise formal relation between them. We believe that this work
will booster the cross-fertilization of the theoretical foundations and the
existing solving methods in both areas. As a step in this direction we provide
a translation from constraint answer set programs with integer linear
constraints to satisfiability modulo linear integer arithmetic that paves the
way to utilizing modern satisfiability modulo theories solvers for computing
answer sets of constraint answer set programs.Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
A Proof Theoretic View of Constraint Programming
We provide here a proof theoretic account of constraint programming that
attempts to capture the essential ingredients of this programming style. We
exemplify it by presenting proof rules for linear constraints over interval
domains, and illustrate their use by analyzing the constraint propagation
process for the {\tt SEND + MORE = MONEY} puzzle. We also show how this
approach allows one to build new constraint solvers.Comment: 25 page
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
Constraint Programming (CP) has proved an effective paradigm to model and
solve difficult combinatorial satisfaction and optimisation problems from
disparate domains. Many such problems arising from the commercial world are
permeated by data uncertainty. Existing CP approaches that accommodate
uncertainty are less suited to uncertainty arising due to incomplete and
erroneous data, because they do not build reliable models and solutions
guaranteed to address the user's genuine problem as she perceives it. Other
fields such as reliable computation offer combinations of models and associated
methods to handle these types of uncertain data, but lack an expressive
framework characterising the resolution methodology independently of the model.
We present a unifying framework that extends the CP formalism in both model
and solutions, to tackle ill-defined combinatorial problems with incomplete or
erroneous data. The certainty closure framework brings together modelling and
solving methodologies from different fields into the CP paradigm to provide
reliable and efficient approches for uncertain constraint problems. We
demonstrate the applicability of the framework on a case study in network
diagnosis. We define resolution forms that give generic templates, and their
associated operational semantics, to derive practical solution methods for
reliable solutions.Comment: Revised versio
Logic-Based Decision Support for Strategic Environmental Assessment
Strategic Environmental Assessment is a procedure aimed at introducing
systematic assessment of the environmental effects of plans and programs. This
procedure is based on the so-called coaxial matrices that define dependencies
between plan activities (infrastructures, plants, resource extractions,
buildings, etc.) and positive and negative environmental impacts, and
dependencies between these impacts and environmental receptors. Up to now, this
procedure is manually implemented by environmental experts for checking the
environmental effects of a given plan or program, but it is never applied
during the plan/program construction. A decision support system, based on a
clear logic semantics, would be an invaluable tool not only in assessing a
single, already defined plan, but also during the planning process in order to
produce an optimized, environmentally assessed plan and to study possible
alternative scenarios. We propose two logic-based approaches to the problem,
one based on Constraint Logic Programming and one on Probabilistic Logic
Programming that could be, in the future, conveniently merged to exploit the
advantages of both. We test the proposed approaches on a real energy plan and
we discuss their limitations and advantages.Comment: 17 pages, 1 figure, 26th Int'l. Conference on Logic Programming
(ICLP'10
Recurrence with affine level mappings is P-time decidable for CLP(R)
In this paper we introduce a class of constraint logic programs such that
their termination can be proved by using affine level mappings. We show that
membership to this class is decidable in polynomial time.Comment: To appear in Theory and Practice of Logic Programming (TPLP
Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price
Given (1) a set of clauses in some first-order language and (2)
a cost function , mapping each
ground atom in the Herbrand base to a non-negative real, then
the problem of finding a minimal cost Herbrand model is to either find a
Herbrand model of which is guaranteed to minimise the sum of the
costs of true ground atoms, or establish that there is no Herbrand model for
. A branch-cut-and-price integer programming (IP) approach to solving this
problem is presented. Since the number of ground instantiations of clauses and
the size of the Herbrand base are both infinite in general, we add the
corresponding IP constraints and IP variables `on the fly' via `cutting' and
`pricing' respectively. In the special case of a finite Herbrand base we show
that adding all IP variables and constraints from the outset can be
advantageous, showing that a challenging Markov logic network MAP problem can
be solved in this way if encoded appropriately
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