Haralick, R., Elliott, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14 (1980) 263--313 Home/Search Document Not in Database Summary Related Articles Check |
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Using Bidirectionality to Speed Up Arc-Consistency.. - Bessière.. (Correct)
....has dramatically increased. It appears that the more constraint networks are used, the simpler the constraint satisfaction techniques involved in the applications are. In fact, a great part of real life applications using constraint networks are limited to a forward checking search procedure [HE80], or use an arc consistency filtering algorithm before or during the search. This is one of the reasons why arc consistency remains a hot area in the CSP community [Bes94a, BC93, Per92, VDT92] Improving the efficiency of arc consistency algorithms improves in the same way the efficiency of all ....
R.M. Haralick and G.L. Elliot. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313, 1980.
Multithreaded Constraint Programming and Applications - Zabatta (Correct)
....via labeling: which variable is to be fixed (variable ordering) and which value it should be fixed at (value ordering) Choosing the proper ordering strategies can greatly improve efficiency of the search. One of the most commonly used strategies for variable ordering is the fail first strategy [HE,80] Here the decision variables with the fewest possible remaining 21 alternatives (the fewest elements in their domains) are selected first. These variables usually are the most difficult to satisfy and can cause failure early and often in the search. The rationale here is that detecting early ....
Haralick, R.; Elliott, G.: Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, 1980.
Planning and Scheduling for Fleets of Earth Observing .. - Frank.. (2001) (2 citations) (Correct)
....since it can only start at time 2. This example suggests a simple rule of thumb for choosing which observation to schedule next: prefer observations having the fewest remaining opportunities. This heuristic resembles the Minimum Remaining Values (MRV) heuristic commonly used in the CSP community [7] . Calculating the number of remaining opportunities for an observation is appealing because it is simple to compute, and provides at least some estimate of how easy it is to schedule that particular observation. However, it does not give any estimate of how much contention there is for those ....
R. Haralick and G. Elliot. Increasing tree search efficiency for constraint satisfaction. Journal of Artificial Intelligence, 14:263--313, 1980.
DisCSPs: Tools for generic and efficient multi-agents negotiations - Hamadi (Correct)
....Which can then eventually start a new forward step. A7 A5 A3 A2 A4 A1 A6 forward step Figure 2. IDIBT: forward steps Figure 2 represents possible forwards steps for the problem of figure 1. Since children share a constraint relation, IDIBT is closer to forward checking search (FC) [8] than to classical backtracking. Here the direct peers connections allow the receiver to prune its subspace according to incoming choices. That is what sequential FC make pro actively between successive instantiations. 3.2.2 Backtrack steps When an agent is unable to perform the first step of a ....
R. M. Haralick and G. L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. AI, 14:263--314, 1980.
A Constraint Processing Approach to Assigning Graduate Teaching.. - Glaubius (2001) (Correct)
....this algorithm was removed from the solver system. Node consistency and Regin s mutex filtering algorithm are the two stand alone constraint propagation algorithms that we ve implemented and used in this system. Other methods for constraint propagation that we have tested are forward checking [6], and forward checking for non binary constraints [2] These methods are used during search; they will be covered in the next section. 4.2 Solution techniques We utilize systematic search techniques based on depth first search to solve the GTA assignment problem. In order to cope with the ....
Robert M. Haralick and Gordon L. Elliott. Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Artificial Intelligence, 14:263--313, 1980.
A Tabu Search Approach for Graph-Structured Case Retrieval - Petrovic, al. (2002) (1 citation) (Correct)
....works in the literature of graph matching, where graphs do not have attribute descriptors (labels) for vertices or arcs. This problem has been studied for over 30 years [19] Various state space search approaches were reported including exact subgraph isomorphism [20] and heuristic techniques [21]. Domain specific heuristic methods have been neglected in the literature for a period due to the development of modern metaheuristic methods [22] such as simulated annealing [23] genetic algorithms [24] and memetic algorithms [25] In [22] Williams and Wilson implement a heuristic method for ....
Haralick, R.M., and Elliott, G., `Increasing tree search efficiency for constraint satisfaction problems', Artificial Intelligence, 14, 263-313, (1980).
A Theoretical Evaluation of Selected Backtracking Algorithms - Grzegorz Kondrak And (1995) (82 citations) (Correct)
....constraint networks. The generic backtracking algorithm was first described more than a century ago, and since then has been rediscovered many times It] In recent years, many new backtracking algorithms have been proposed. The basic ones include Backmarking [3] Backjumping [4] Forward Checking [5], and Conflict Directed Backjumping It0] Several hybrid algorithms, which combine two or more basic algorithms, have also been developed It0] There is no simple answer to the question which backtracking algorithm is the best one. First, the performance of backtracking algorithms depends heavily ....
....variable xi, CBJ backtracks to the highest variable xn in the conflict set of xi. At the same time, the conflict set of xi is absorbed by the conflict set of xn, so that no information about conflicts is lost. In contrast with the above backward checking al gorithms, Forward Checking (FC) [5] performs con sistency checks forward, that is, between the current variable and the future variables. After the current variable has been instantJared, the domains of the future variables are filtered in such a way that all values inconsistent with the current instantiation are removed. If none ....
R. M. Haralick and G. L. Elliot. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263-314, 1980.
The Progressive Party Problem: Linear Programming and.. - Smith, Brailsford, al. (Correct)
....provides a default backtracking search method for solving CSPs. If necessary, new constraint classes and search algorithms can be defined, but these facilities were not required in this case. The default search algorithm is an implementation of full lookahead, as described by Haralick and Elliott [2]. The algorithm repeatedly chooses an unassigned variable, chooses a value for that variable from its current domain and makes a tentative assignment. The constraints are then used to identify the effects of the assignment on future (still unassigned) variables, and any value in the domain of a ....
R. Haralick and G. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313, 1980.
From Restricted Path Consistency to Max-Restricted Path.. - Debruyne, Bessière (1997) (6 citations) (Correct)
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Haralick, R., Elliott, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14 (1980) 263--313
CSAA: A Constraint Satisfaction Ant Algorithm Framework - Mertens, Holvoet (2004) (Correct)
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Robert M. Haralick and Gordon L. Elliot. Increasing Tree Search Efficiency for Constraint Satisfaction Problems. Artificial Intelligence, 14(3):263--313, 1980.
Consistency Techniques for Linear Arithmetic and Functional.. - Zhang (1998) (Correct)
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Haralick, R.M., and Elliott, G.L., Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence , Vol14, 263-313, 1980
A Comparative Study of Eight Constraint Programming Languages .. - Fernandez, Hill (2000) (Correct)
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Haralick, R. & Elliot, G. (1980). Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14: 263--313.
Constraints, Varieties, and Algorithms - van Dongen (2002) (Correct)
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R.M. Haralick and G.L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14(3):263--313, 1980.
Trying Again to Fail-First - Beck, Prosser, Wallace (Correct)
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Haralick, R.M., Elliott, G.L.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14 (1980) 263--314
Ordinal Constraint Satisfaction - Eugene Freuder Richard (Correct)
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R. M. Haralick and G. L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313, 1980.
Toward Understanding Variable Ordering Heuristics for - Constraint Satisfaction.. (Correct)
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R. M. Haralick and G. L. Elliot, `Increasing tree search efficiency for constraint satisfaction problems', Artif. Intell., 14, 263--314, (1980).
Forward Checking with Backmarking - June Patrick Prosser (1995) (13 citations) (Correct)
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R.M. Haralick and G.L. Elliott, Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artif. Intell. 14 (1980) 263-313
Fuzzy rrDFCSP and planning - Miguel, Shen (2003) (1 citation) (Correct)
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R. Haralick, G. Elliot, Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence 14 (1980) 263--313.
Compositional Ecological Modelling via Dynamic Constraint.. - Keppens (Correct)
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Haralick, R.M. and Elliot, G.L. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14:263--313, 1980.
Scaling Effects in the CSP Phase Transition - Ian Gent Ewan (1995) (11 citations) (Correct)
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R.M. Haralick and G.L. Elliott, Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artif. Intell. 14 (1980) 263-313.
Constraints, Varieties, and Algorithms - van Dongen (2002) (Correct)
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R.M. Haralick and G.L. Elliott. Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, 14(3):263--313, 1980.
Planning and Scheduling for Fleets of Earth Observing .. - Frank.. (2001) (2 citations) (Correct)
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R. Haralick and G. Elliot. Increasing tree search efficiency for constraint satisfaction. Journal of Artificial Intelligence, 14:263--313, 1980.
Planning and Scheduling for Fleets of Earth Observing .. - Frank.. (2001) (2 citations) (Correct)
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R. Haralick and G. Elliot. Increasing tree search efficiency for constraint satisfaction. Journal of Artificial Intelligence, 14:263--313, 1980.
CBack: A Simple Tool for Backtrack Programming in C - Helsgaun (1995) (Correct)
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R. M. Haralick and G. L. Elliot, `Increasing tree search efficiency for constraint satisfaction problems', Artificial Intelligence, 14, 263--313 (1980).
From ALife Agents to a Kingdom of N Queens - Jing, Liu, Al. (1999) (1 citation) (Correct)
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R. M. Haralick, etc., Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence, Vol.14, (1980) pp.263-313.
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