Dechter, R., Pearl, J. Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34:1-38, 1988. Home/Search Document Not in Database Summary Related Articles Check |
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....the fact that there is only a path without loops between each pair of variables X i , X j (not directly constrained) and this path gives the minimal constraint between X i , X j . Original constraints of the tree are yet minimal. To obtain a solution in the tree, we can use a method proposed by [3] for searching solutions in CSP with tree structure. The method starts instantiating the root variable, corresponding to time origin, X 0 . Next, we obtain a preorder list of the variables, and take one of the variables of this list in each step. The assignation of the value is done looking at the ....
R. Dechter and J. Pearl. Network-based heuristics for constraint-satisfaction problems, Artificial Intelligence, 34, 1988, pp. 1-38.
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R. Dechter and J. Pearl. Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34:1--38, 1987.
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R. Dechter and J. Pearl. Network-based heuristics for constraint satisfaction problems. Artificial Intelligence, 34:1--38, 1987.
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....node , arc , and path consistency [48] correspond to 1 , 2 , and 3consistency, respectively. Given an ordering of the variables, the network is directional k consistent i any subset of k 1 variables is k consistent relative 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 ....
....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 ....
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