51,644 research outputs found
Consistency and Random Constraint Satisfaction Models
In this paper, we study the possibility of designing non-trivial random CSP
models by exploiting the intrinsic connection between structures and
typical-case hardness. We show that constraint consistency, a notion that has
been developed to improve the efficiency of CSP algorithms, is in fact the key
to the design of random CSP models that have interesting phase transition
behavior and guaranteed exponential resolution complexity without putting much
restriction on the parameter of constraint tightness or the domain size of the
problem. We propose a very flexible framework for constructing problem
instances withinteresting behavior and develop a variety of concrete methods to
construct specific random CSP models that enforce different levels of
constraint consistency. A series of experimental studies with interesting
observations are carried out to illustrate the effectiveness of introducing
structural elements in random instances, to verify the robustness of our
proposal, and to investigate features of some specific models based on our
framework that are highly related to the behavior of backtracking search
algorithms
Proteus: A Hierarchical Portfolio of Solvers and Transformations
In recent years, portfolio approaches to solving SAT problems and CSPs have
become increasingly common. There are also a number of different encodings for
representing CSPs as SAT instances. In this paper, we leverage advances in both
SAT and CSP solving to present a novel hierarchical portfolio-based approach to
CSP solving, which we call Proteus, that does not rely purely on CSP solvers.
Instead, it may decide that it is best to encode a CSP problem instance into
SAT, selecting an appropriate encoding and a corresponding SAT solver. Our
experimental evaluation used an instance of Proteus that involved four CSP
solvers, three SAT encodings, and six SAT solvers, evaluated on the most
challenging problem instances from the CSP solver competitions, involving
global and intensional constraints. We show that significant performance
improvements can be achieved by Proteus obtained by exploiting alternative
view-points and solvers for combinatorial problem-solving.Comment: 11th International Conference on Integration of AI and OR Techniques
in Constraint Programming for Combinatorial Optimization Problems. The final
publication is available at link.springer.co
Stochastic Constraint Programming
To model combinatorial decision problems involving uncertainty and
probability, we introduce stochastic constraint programming. Stochastic
constraint programs contain both decision variables (which we can set) and
stochastic variables (which follow a probability distribution). They combine
together the best features of traditional constraint satisfaction, stochastic
integer programming, and stochastic satisfiability. We give a semantics for
stochastic constraint programs, and propose a number of complete algorithms and
approximation procedures. Finally, we discuss a number of extensions of
stochastic constraint programming to relax various assumptions like the
independence between stochastic variables, and compare with other approaches
for decision making under uncertainty.Comment: Proceedings of the 15th Eureopean Conference on Artificial
Intelligenc
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