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Constraint propagation
 Handbook of Constraint Programming
, 2006
"... Constraint propagation is a form of inference, not search, and as such is more ”satisfying”, both technically and aesthetically. —E.C. Freuder, 2005. Constraint reasoning involves various types of techniques to tackle the inherent ..."
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Cited by 68 (5 self)
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Constraint propagation is a form of inference, not search, and as such is more ”satisfying”, both technically and aesthetically. —E.C. Freuder, 2005. Constraint reasoning involves various types of techniques to tackle the inherent
Characterising Tractable Constraints
 Artificial Intelligence
, 1994
"... Finding solutions to a binary constraint satisfaction problem is known to be an NPcomplete problem in general, but may be tractable in cases where either the set of allowed constraints or the graph structure is restricted. This paper considers restricted sets of contraints which are closed under pe ..."
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Cited by 57 (18 self)
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Finding solutions to a binary constraint satisfaction problem is known to be an NPcomplete problem in general, but may be tractable in cases where either the set of allowed constraints or the graph structure is restricted. This paper considers restricted sets of contraints which are closed under permutation of the labels. We identify a set of constraints which gives rise to a class of tractable problems and give polynomial time algorithms for solving such problems, and for finding the equivalent minimal network. We also prove that the class of problems generated by any set of constraints not contained in this restricted set is NPcomplete. 1 Introduction Finding solutions to a constraint satisfaction problem is known to be an NPcomplete problem in general [11] even when the constraints are restricted to binary constraints. However, many of the problems which arise in practice have special properties which allow them to be solved efficiently. The question of identifying restrictions t...
Intelligent Backtracking On Constraint Satisfaction Problems: Experimental And Theoretical Results
, 1995
"... The Constraint Satisfaction Problem is a type of combinatorial search problem of much interest in Artificial Intelligence and Operations Research. The simplest algorithm for solving such a problem is chronological backtracking, but this method suffers from a malady known as "thrashing," in ..."
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Cited by 52 (0 self)
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The Constraint Satisfaction Problem is a type of combinatorial search problem of much interest in Artificial Intelligence and Operations Research. The simplest algorithm for solving such a problem is chronological backtracking, but this method suffers from a malady known as "thrashing," in which essentially the same subproblems end up being solved repeatedly. Intelligent backtracking algorithms, such as backjumping and dependencydirected backtracking, were designed to address this difficulty, but the exact utility and range of applicability of these techniques have not been fully explored. This dissertation describes an experimental and theoretical investigation into the power of these intelligent backtracking algorithms. We compare the empirical performance of several such algorithms on a range of problem distributions. We show that the more sophisticated algorithms are especially useful on those problems with a small number of constraints that happen to be difficult for chronologica...
On the Minimality and Global Consistency of RowConvex Constraint Networks
, 1992
"... Constraint networks have beenshown to be useful in formulating such diverse problems as scene labeling, natural language parsing, and temporal reasoning. Given a constraint network, we often wish to (i) nd a solution that satis es the constraints and (ii) nd the corresponding minimal network where t ..."
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Cited by 51 (3 self)
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Constraint networks have beenshown to be useful in formulating such diverse problems as scene labeling, natural language parsing, and temporal reasoning. Given a constraint network, we often wish to (i) nd a solution that satis es the constraints and (ii) nd the corresponding minimal network where the constraints are as explicit as possible. Both tasks are known to be NPcomplete in the general case. Task (i) is usually solved using a backtracking algorithm, and task (ii) is often solved only approximately by enforcing various levels of local consistency. In this paper, we identify a property of binary constraints called row convexity and show its usefulness in deciding when a form of local consistency called path consistency is sufficient to guarantee that a network is both minimal and globally consistent. Globally consistent networks have the property that a solution can be found without backtracking. We show that one can test for the row convexity property e ciently and we show, by examining
Hentenryck, Constraint satisfaction over connected row convex constraints
 in: Proc. IJCAI97
, 1997
"... This paper studies constraint satisfaction over connected rowconvex (CRC) constraints. It shows that CRC constraints are closed under composition, intersection, and transposition, the basic operations of pathconsistency algorithms. This establishes that path consistency over CRC constraints produc ..."
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Cited by 28 (0 self)
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This paper studies constraint satisfaction over connected rowconvex (CRC) constraints. It shows that CRC constraints are closed under composition, intersection, and transposition, the basic operations of pathconsistency algorithms. This establishes that path consistency over CRC constraints produces a minimal and decomposable network and is thus a polynomialtime decision procedure for CRC networks. This paper also presents a new pathconsistency algorithm for CRC constraints running in time O(n3d2) and space O(n2d), wherenisthenumber of variables and d is the size of the largest domain, improving the traditional time and space complexity by orders of magnitude. The paper also shows how to construct CRC constraints by conjunction and disjunction of a set of basic CRC constraints, highlighting how CRC constraints generalize monotone constraints and presenting interesting subclasses of CRC constraints. Experimental results show that the algorithm behaves well in practice. © 1999 Elsevier Science B.V. All rights reserved.
Dynamic Flexible Constraint Satisfaction and its Application to AI Planning
, 2001
"... Constraint satisfaction is a fundamental Artificial Intelligence technique for knowledge representation and inference. It has, however, become clear that the original formulation of a static constraint satisfaction problem (CSP) with hard, imperative constraints is insucient to model many real prob ..."
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Cited by 22 (6 self)
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Constraint satisfaction is a fundamental Artificial Intelligence technique for knowledge representation and inference. It has, however, become clear that the original formulation of a static constraint satisfaction problem (CSP) with hard, imperative constraints is insucient to model many real problems. Recent work has addressed these shortcomings in the form of two separate extensions known as dynamic CSP and flexible CSP respectively. Little has yet been done to combine dynamic and exible CSP in order to bring to bear the benefits of both in solving more complex problems. Based on a
Improving Domain Filtering using Restricted Path Consistency
 In Proceedings of IEEE CAIA95
, 1995
"... This paper introduces a new level of partial consistency for constraint satisfaction problems, which is situated between arc and pathconsistency. We call this level restricted pathconsistency (rpc). Two procedures to enforce complete and partial rpc are presented. They both use pathbased verifica ..."
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Cited by 20 (0 self)
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This paper introduces a new level of partial consistency for constraint satisfaction problems, which is situated between arc and pathconsistency. We call this level restricted pathconsistency (rpc). Two procedures to enforce complete and partial rpc are presented. They both use pathbased verifications to detect inconsistencies but retain the good features of arcconsistency since they only touch the variables domains and do not augment the connectivity of the constraint graph. We show that, although they perform a limited number of checks, these procedures exhibit a considerable pruning power. 1 Introduction Constraint satisfaction problems (csp) have proved useful to encode various instances of combinatorial problems. A csp is simply defined by giving a set of variables, each having a finite domain, and a set of constraints, each connected to a subset of the variables. Constraints are partial informations that restrict the values that can be assigned simultaneously to their variabl...
The rough guide to constraint propagation
 In 5th International Conference on Principles and Practice of Constraint Programming (CP’99
, 1999
"... Abstract. We provide here a simple, yet very general framework that allows us to explain several constraint propagation algorithms in a systematic way. In particular, using the notions commutativity and semicommutativity, we show how the wellknown AC3, PC2, DAC and DPC algorithms are instances o ..."
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Cited by 19 (2 self)
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Abstract. We provide here a simple, yet very general framework that allows us to explain several constraint propagation algorithms in a systematic way. In particular, using the notions commutativity and semicommutativity, we show how the wellknown AC3, PC2, DAC and DPC algorithms are instances of a single generic algorithm. The work reported here extends and simplifies that of Apt [1]. 1
Theory and practice of constraint propagation
 In Proceedings of the 3rd Workshop on Constraint Programming in Decision and Control
, 2001
"... Abstract: Despite successful application of constraint programming (CP) to solving many reallife problems there is still an indispensable group or researchers considering (wrongly) CP as a simple evaluation technique only. Even if sophisticated search algorithms play an important role in solving co ..."
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Cited by 18 (2 self)
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Abstract: Despite successful application of constraint programming (CP) to solving many reallife problems there is still an indispensable group or researchers considering (wrongly) CP as a simple evaluation technique only. Even if sophisticated search algorithms play an important role in solving constraintbased models, the real power engine behind CP is called constraint propagation (domain filtering, pruning or consistency techniques). In the paper we give a survey of common consistency techniques for binary constraints. We describe the main ideas behind them, list their advantages and limitations, and compare their pruning power. Then we briefly explain how these techniques can be extended to nonbinary constraints. Last part of the paper is devoted to modelling issues. We give some hints how the constraint propagation can be exploited more when solving reallife problems. This part is based on our experience with solving reallife programs and it is also supported by empirical observations of other researchers.
Path Consistency for Triangulated Constraint Graphs
 In Proc. of the 16 �¡ IJCAI
, 1999
"... bliekQilog.fr Among the local consistency techniques used in the resolution of constraint satisfaction problems (CSPs), path consistency (PC) has received a great deal of attention. A constraint graph G is PC if for any valuation of a pair of variables that satisfy the constraint in G between them, ..."
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Cited by 15 (0 self)
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bliekQilog.fr Among the local consistency techniques used in the resolution of constraint satisfaction problems (CSPs), path consistency (PC) has received a great deal of attention. A constraint graph G is PC if for any valuation of a pair of variables that satisfy the constraint in G between them, one can find values for the intermediate variables on any other path in G between those variables so that all the constraints along that path are satisfied. On complete graphs, Montanari showed that PC holds if and only if each path of length two is PC. By convention, it is therefore said that a CSP is PC if the completion of its constraint graph is PC. In this paper, we show that Montanari's theorem extends to triangulated graphs. One can therefore enforce PC on sparse graphs by triangulating instead of completing them. The advantage is that with triangulation much less universal constraints need to be added. We then compare the pruning capacity of the two approaches. We show that when the constraints are convex, the pruning capacity of PC on triangulated graphs and their completion are identical on the common edges. Furthermore, our experiments show that there is little difference for general nonconvex problems. 1