17,819 research outputs found
High performance constraint satisfaction problem solving: State-recomputation versus state-copying.
Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005
Minimal Forward Checking--a Lazy Constraint Satisfaction Search Algorithm: Experimental And Theoretical Results
Many problems that occur in Artificial Intelligence and Operations Research can be naturally represented as Constraint Satisfaction Problems (CSPs). One of the most popular backtracking search algorithms used to solve CSPs is called Forward Checking (FC). FC performs a limited amount of lookahead during its search attempting to detect future inconsistencies thereby avoiding inconsistent parts of the search tree. In this thesis we describe a new backtracking search algorithm called Minimal Forward Checking (MFC) which maintains FC\u27s ability to detect inconsistencies but which is lazy in is method of doing so. We prove that MFC is sound and complete. We also prove the MFC and FC visit the same nodes in the search tree. Most significantly, we prove that MFC\u27s worst case performance in terms of number of constraint checks performed (the common measure of performance of these algorithms) is the number of constraint checks performed by FC. We then describe how the MFC algorithm can be seen as one algorithm in a family of lazy CSP search algorithms.;As theoretical results on the average case complexity for CSP search algorithms are extremely difficult to derive, empirical comparisons need to be performed. A commonly used testbed is randomly generated problems drawn from a standard model of binary CSPs at a specific location known to contain problems that are relatively hard to solve. We generalize the standard model of binary CSPs and show how to find problems in this model that are relatively hard to solve. We also show that these hard problems are of similar hardness or harder than hard problems drawn from the standard model especially as the problem size grows and the problem has a relatively sparse structure. We perform large empirical studies of many CSP search algorithms including variants of MFC and FC with non-chronological backtracking and variants of the Fail First heuristic on two testbeds of hard random problems, each drawn from one of the two models. Our empirical comparisons on both testbeds indicate that the average case performance of algorithms based on MFC are better than all the other algorithms in the comparison in terms of the number of constraint checks performed
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
A Partial Taxonomy of Substitutability and Interchangeability
Substitutability, interchangeability and related concepts in Constraint
Programming were introduced approximately twenty years ago and have given rise
to considerable subsequent research. We survey this work, classify, and relate
the different concepts, and indicate directions for future work, in particular
with respect to making connections with research into symmetry breaking. This
paper is a condensed version of a larger work in progress.Comment: 18 pages, The 10th International Workshop on Symmetry in Constraint
Satisfaction Problems (SymCon'10
Layered Fixed Point Logic
We present a logic for the specification of static analysis problems that
goes beyond the logics traditionally used. Its most prominent feature is the
direct support for both inductive computations of behaviors as well as
co-inductive specifications of properties. Two main theoretical contributions
are a Moore Family result and a parametrized worst case time complexity result.
We show that the logic and the associated solver can be used for rapid
prototyping and illustrate a wide variety of applications within Static
Analysis, Constraint Satisfaction Problems and Model Checking. In all cases the
complexity result specializes to the worst case time complexity of the
classical methods
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Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
This paper presents an experimental evaluation of two orthogonal schemes for preprocessing constraint satisfaction problems (CSPs). The first of these schemes involves a class of local consistency techniques that includes directional arc consistency, directional path consistency, and adaptive consistency. The other scheme concerns the prearrangement of variables in a linear order to facilitate an efficient search. In the first series of experiments, we evaluated the effect of each of the local consistency techniques on backtracking and its common enhancement, backjumping. Surprizingly, although adaptive consistency has the best worst-case complexity bounds, we have found that it exhibits the worst performance, unless the constraint graph was very sparse. Directional arc consistency (followed by either backjumping or backtracking) and backjumping (without any pre-processing) outperformed all other techniques; moreover, the former dominated the latter in computationally intensive situations. The second series of experiments suggests that maximum cardinality and minimum width arc the best pre-ordering (i.e., static ordering) strategies, while dynamic search rearrangement is superior to all the preorderings studied
Intelligent search strategies based on adaptive Constraint Handling Rules
The most advanced implementation of adaptive constraint processing with
Constraint Handling Rules (CHR) allows the application of intelligent search
strategies to solve Constraint Satisfaction Problems (CSP). This presentation
compares an improved version of conflict-directed backjumping and two variants
of dynamic backtracking with respect to chronological backtracking on some of
the AIM instances which are a benchmark set of random 3-SAT problems. A CHR
implementation of a Boolean constraint solver combined with these different
search strategies in Java is thus being compared with a CHR implementation of
the same Boolean constraint solver combined with chronological backtracking in
SICStus Prolog. This comparison shows that the addition of ``intelligence'' to
the search process may reduce the number of search steps dramatically.
Furthermore, the runtime of their Java implementations is in most cases faster
than the implementations of chronological backtracking. More specifically,
conflict-directed backjumping is even faster than the SICStus Prolog
implementation of chronological backtracking, although our Java implementation
of CHR lacks the optimisations made in the SICStus Prolog system. To appear in
Theory and Practice of Logic Programming (TPLP).Comment: Number of pages: 27 Number of figures: 14 Number of Tables:
An Algebraic Preservation Theorem for Aleph-Zero Categorical Quantified Constraint Satisfaction
We prove an algebraic preservation theorem for positive Horn definability in
aleph-zero categorical structures. In particular, we define and study a
construction which we call the periodic power of a structure, and define a
periomorphism of a structure to be a homomorphism from the periodic power of
the structure to the structure itself. Our preservation theorem states that,
over an aleph-zero categorical structure, a relation is positive Horn definable
if and only if it is preserved by all periomorphisms of the structure. We give
applications of this theorem, including a new proof of the known complexity
classification of quantified constraint satisfaction on equality templates
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