A. Mackworth and E. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--74, 1985. Home/Search Document Not in Database Summary Related Articles Check |
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Constraint Networks - Dechter (1992) (82 citations) (Correct)
....(224; 426)g. Viewed in this way, any network can be solved by binary networks techniques. 3. 2 Solving Tree Networks Almost all the known structure based techniques rely on the observation that binary constraint networks whose constraint graph is a tree can be solved in linear time [Freuder 82, Mackworth Freuder 84b, Dechter Pearl 87] 7 The solution of tree structured networks are discussed, and later it is shown how they can be used to facilitate the solution of general CN . Given a tree network over n variables (Fig. 4a) the rst step of the treealgorithm is to generate a rooted directed tree. Each ....
Mackworth, A. K., and Freuder, E. C., \The complexity of some polynomial network consistency algorithms for constraint satisfaction problems," Arti cial Intelligence, Vol. 25, No. 1, 1984.
Using Bidirectionality to Speed Up Arc-Consistency.. - Bessière.. (Correct)
....that the AC 6 arc consistency algorithm is optimal in time on constraint networks where nothing is known about the constraint semantics. However, in constraint networks, it is always assumed that constraints are bidirectional. None of the previous algorithms achieving arc consistency (AC 3 [Mac77, MF85], AC 4 [MH86] AC 6) use constraint bidirectionality. We propose here an improved version of AC 6 which uses this property. Then, we claim that our new algorithm is optimal in the number of constraint checks performed (i.e. given a variable, value, and arc ordering, it performs the minimum ....
.... 34392 Montpellier Cedex 5, FRANCE e mail : regin lirmm.fr combination of values a for a variable i and b for a variable j is allowed by the constraint between i and j, then the combination b for j and a for i is allowed by the constraint between j and i) and none of the previous algorithms (AC 3 [Mac77, MF85], AC 4 [MH86] and AC 6) use this fact . In this paper, we propose AC6 , an improved version of AC 6 which uses constraint bidirectionality. We claim that AC6 is optimal in the number of constraint checks performed . Moreover, AC6 avoids the explosion of space complexity, keeping it at ....
A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65--74, 1985.
Backjump-based Backtracking for Constraint Satisfaction Problems - Dechter, Frost (2001) (3 citations) (Correct)
....to every single variable that succeeds the k 1 variables in the ordering [20] A problem that is k consistent for all k is called globally consistent. A variety of algorithms have been developed for enforcing di erent levels of local consistency, which is also called constraint propagation [50,58,14,79,8,20]. For example, arc consistency algorithms can delete values from the domains of variables, to ensure that each value in the domain of each variable is consistent with at least one value in the domain of each other variable. Path consistency is achieved by introducing new constraints or nogoods ....
....Tractable classes of constraint networks are generally recognized by realizing that for some problems, enforcing low level consistency (in polynomial time) guarantees global consistency and therefore a solution to the problem. The basic graph structure that supports tractability is a tree [50]. In particular, enforcing 2 consistency on a tree structured binary CSP network ensures a solution with no dead ends along some recognizable ordering of the variables. A popular class of incomplete algorithms are stochastic methods which typically move in a hill climbing manner augmented with ....
A. K. Mackworth and E. C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Arti cial Intelligence, 25:65-74, 1985.
AC-3_d an Efficient Arc-Consistency Algorithm with a Low.. - van Dongen (2002) (Correct)
....double support checks [van Dongen, 2002] 2. 2 Related Literature In 1977 Mackworth presented an arc consistency algorithm called AC 3 [Mackworth, 1977] Together with Freuder he presented a lower bound of (ed ) and an upper bound of O (ed ) for its worst case time complexity [Mackworth and Freuder, 1985] . The algorithm, as mentioned before, has a O (e nd) space complexity. AC 3, as presented by Mackworth, is not an algorithm as such; it is a class of algorithms which have certain data structures in common and treat them similarly. The most prominent data structure used by AC 3 is a queue ....
A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--73, 1985.
Design and Implementation of a Parallel Constraint - Satisfaction Algorithm Marco (Correct)
....solved by backtracking algorithms which explore the search space of the CSP by a depth first search. Many improvements to simple backtracking algorithms have been developed to solve CSPs more efficiently. These improvements range from preprocessing steps, like arcand path consistency algorithms [13] [14] to advanced search techniques [18] In QSim, the basic algorithm to solve the CSP is simple backtracking [18] Increased performance is achieved by an arc consistency algorithm that reduces the search space and by a heuristic 1 (a) constraint network (b) dual constraint ....
....partitioning the tuple sets in an arbitrary manner is that many subproblems can be discarded from further processing. VBP avoids the generation of subproblems with tuple subsets using mutually exclusive variable values. These subproblems violate local consistency conditions, i.e. arc consistency [13]. There cannot be any solution in these subproblems. The number of subproblems which must be searched with VBP is given by the number of subdomains. If k subdomains are generated for a variable with q attached constraints, only k subproblems must be searched for solutions. In contrast, if each ....
A. K. Mackworth and E. C. Freuder. "The Complexity of Some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems". Artificial Intelligence, 25:65--74, 1985. 32
The Complexity of Constraint Satisfaction Problems For Small.. - Hirsch (Correct)
....1970s onwards, computer scientists working in planning [3,4] and temporal reasoning [5 11] rediscovered relation algebra. Later, scholars working in the field of Knowledge Representation, and specifically Spatial and Temporal Knowledge Representation, also used the formalism of relation algebra [10,12 18]. For them, the principal method of reasoning using a relation algebra was by checking the consistency of a set of constraints over that algebra. So this work became integrated with a wider study of constraint handling in computer science [19 24] This problem of determining the satisfiability of ....
A. Mackworth, C. Freuder, The complexity of some polynomial network consistency algorithms for constraint satisfation problems, Artificial Intelligence 25 (1) (1985) 65--74.
Constraint Propagation - Bessiere (2006) Self-citation (Freuder) (Correct)
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A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65--74, 1985.
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A. Mackworth and E. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--74, 1985.
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Mackworth,A. K. and Freud,E.C.,The Complexity of Some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems, Artificial Intelligence 25,65-74,1985
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Mackworth,A. K. and Freud,E.C.,The Complexity of Some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems, Artificial Intelligence 25,65-741 1985
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A. K. Mackworth and E. C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65--74, 1985.
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Alan K. Mackworth and Eugene C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Arti cial Intelligence, 25(1):65{ 74, 1985.
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A. K. Mackworth and E. C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65--74, 1985.
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A.K. Mackworth, E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems, Artificial Intelligence 25 p65--74. (1985).
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A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--73, 1985. 25
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A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--73, 1985.
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A.K. Mackworth and E.C. Freuder. The Complexity of some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems. Arti cial Intelligence, 25:65-74, 1985. 167
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A. Mackworth and E. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--74, 1985.
Satisfiability of Quantitative Temporal - Constraints With Multiple (Correct)
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A. K. Mackworth and E. C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25:65--74, 1985.
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A. Mackworth and E. Freuder (1985). The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence Journal 25.
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A. K. Mackworth and E. C. Freuder, The Complexity of Some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems, Journal of Artificial Intelligence 25:65-74, 1985.
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Mackworth, A.K., and Freuder, E.C., The Complexity of Some Polynomial Network Consistency Algorithms for Constraint Satisfaction Problems, Artificial Intelligence 25, pp65--74, 1985.
Constraints, Varieties, and Algorithms - van Dongen (2002) (Correct)
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A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--73, 1985.
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A.K. Mackworth and E.C. Freuder. The complexity of some polynomial network consistency algorithms for constraint satisfaction problems. Artificial Intelligence, 25(1):65--73, 1985.
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