9,467 research outputs found
New Light on Arc Consistency over Continuous Domains
Hyvönen and Faltings observed that propagation algorithms with continuous variables are computationally extremely inefficient when unions of intervals are used to precisely store refinements of domains. These algorithms were designed in the hope of obtaining the interesting property of arc consistency, that guarantees every value in domains to be consistent w.r.t. every constraint. In the first part of this report, we show that a pure backtrack-free filtering algorithm enforcing arc consistency will never exist. But surprisingly, we show that it is easy to obtain a property stronger than arc consistency with a few steps of bisection. We define this so-called box-set consistency and detail a generic method to enforce it. In the second part, a concrete algorithm, derived from the lazy version of the generic method is proposed. Correctness is proved and the properties are studied precisely
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
A Logical Approach to Efficient Max-SAT solving
Weighted Max-SAT is the optimization version of SAT and many important
problems can be naturally encoded as such. Solving weighted Max-SAT is an
important problem from both a theoretical and a practical point of view. In
recent years, there has been considerable interest in finding efficient solving
techniques. Most of this work focus on the computation of good quality lower
bounds to be used within a branch and bound DPLL-like algorithm. Most often,
these lower bounds are described in a procedural way. Because of that, it is
difficult to realize the {\em logic} that is behind.
In this paper we introduce an original framework for Max-SAT that stresses
the parallelism with classical SAT. Then, we extend the two basic SAT solving
techniques: {\em search} and {\em inference}. We show that many algorithmic
{\em tricks} used in state-of-the-art Max-SAT solvers are easily expressable in
{\em logic} terms with our framework in a unified manner.
Besides, we introduce an original search algorithm that performs a restricted
amount of {\em weighted resolution} at each visited node. We empirically
compare our algorithm with a variety of solving alternatives on several
benchmarks. Our experiments, which constitute to the best of our knowledge the
most comprehensive Max-sat evaluation ever reported, show that our algorithm is
generally orders of magnitude faster than any competitor
A CHR-based Implementation of Known Arc-Consistency
In classical CLP(FD) systems, domains of variables are completely known at
the beginning of the constraint propagation process. However, in systems
interacting with an external environment, acquiring the whole domains of
variables before the beginning of constraint propagation may cause waste of
computation time, or even obsolescence of the acquired data at the time of use.
For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model
has been proposed as an extension of the CSP model, to make it possible to
start constraint propagation even when domains are not fully known, performing
acquisition of domain elements only when necessary, and without the need for
restarting the propagation after every acquisition.
In this paper, we show how a solver for the two sorted CLP language, defined
in previous work, to express ICSPs, has been implemented in the Constraint
Handling Rules (CHR) language, a declarative language particularly suitable for
high level implementation of constraint solvers.Comment: 22 pages, 2 figures, 1 table To appear in Theory and Practice of
Logic Programming (TPLP
Improving the Asymmetric TSP by Considering Graph Structure
Recent works on cost based relaxations have improved Constraint Programming
(CP) models for the Traveling Salesman Problem (TSP). We provide a short survey
over solving asymmetric TSP with CP. Then, we suggest new implied propagators
based on general graph properties. We experimentally show that such implied
propagators bring robustness to pathological instances and highlight the fact
that graph structure can significantly improve search heuristics behavior.
Finally, we show that our approach outperforms current state of the art
results.Comment: Technical repor
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