Eugene C. Freuder and Michael J. Quinn. Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of International Joint Conference on Artificial Intellgence, pages 1076--1078, 1985.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....component is completely independent of the others. We apply this idea recursively as Davis Putnam builds a partial satisfying assignment. The idea of recursively exploiting connected components in solving instances of SAT or the more general constraint satisfaction problem is not entirely new [Freuder Quinn 1985; Bayardo Miranker 1995] In these formulations, however, connected components are identified using the full constraint graph, prior to attempting any solution of the instance. While this method is well suited for obtaining worst case complexity bounds for determining satisfiability given ....

Freuder, E.C. and Quinn, M.J., 1985. Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems. In Proceedings of IJCAI-85, 1076-1078.

.... problems such as Predicate Calculus [2] Propositional Logic [3] Truth Maintenance [4] Integer Programming [5] Automata Theory [6] Graph Theory [7] Hill Climbing [8] Neural Networks [9] Genetic Algorithms [10] Relational Algebra [11] Constraint Synthesis [12] Disjunctive Decomposition [13], Conjunctive Decomposition [14] Constraint Logic Programming [15] and GSAT [16] We can classify these techniques into (1) problem reduction, 2) solution synthesis, and (3) searching. In problem reduction, we first identify redundant information and then remove them. On the contrary to problem ....

E. C. Freuder and M. Quinn, "Taking advantage of stable sets of variables in constraintsatisfaction problems," in Proceedings of the Ninth International Joint Conference on Artificial Intelligence, (Menlo Park, California), 1985.

....a small constant of , and that dedicating more space to learning further reduces the difference in runtime bounds. Our results are similar to several other structure exploiting techniques. What distinguishes this work from the cycle cutset method (Dechter 1990) and graph splitting techniques (Freuder Quinn 1985; Bayardo Miranker 1995) is we do not require positioning variables with few constraints between them first in the ordering in order to effectively bound runtime. We demonstrate that our techniques provide good bounds across a wide variety of graph arrangement policies. Unlike schemes that ....

....variables with few constraints between them first in the ordering in order to effectively bound runtime. We demonstrate that our techniques provide good bounds across a wide variety of graph arrangement policies. Unlike schemes that exploit nonseparable components of the constraint graph (Freuder 1985; Dechter Pearl 1987) our algorithms are backtrack driven and require no exponential time preprocessing phases. They thereby preserve good performance on easy instances and allow application of additional proven backtrack enhancement schemes including lookahead (Nadel 1988) conflictdirected ....

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Freuder, E. C. and Quinn, M. J. 1985. Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems. In Proc. Ninth Intl. Joint Conf. on Artificial Intelligence, 10761078.

....query processing should strive to maintain the specific statistics we have described. This work remains to be extended to cyclic queries, though we have ideas on how this might be accomplished. In the cyclic case, query graphs can still be arranged into rooted trees for rooted tree pipelining [9]. The probabilistic analysis will have to be extended to consider that tuplelookups along more than one predicate may be required at any one stage in the pipe. The work could also be extended to consider cases where non pipelined methods may be appropriate. For instance, two small relations may be ....

E. C. Freuder, and M. J. Quinn, Taking advantage of stable sets of variables in constraint satisfaction problems. In Proc. Int'l Joint Conf. on Artificial Intelligence, 1076-1078, 1985.

....and selection of new values are complex and costly. In addition, the focus of dependency backtracking will remain narrow and, thus, useful only as long as the number of interacting variables is significantly less than the number of all variables in the CSP. In the literature (e.g. [34, 109]) one finds a variety of heuristics for the run time ordering of variables and values. Examples of such heuristics are (1) choose the variable with the fewest possible remaining alternative values , 2) choose the most constrained variable , or (3) choose a variable and a value such that the ....

E. Freuder and M. Quinn. Taking Advantage of Stable Sets of Variables in Constraint-Satisfaction Problems. In Proceedings of IJCAI-85, Menlo Park, CA, 1985.

....as it also renders a CSP backtrackfree. Its main advantage is that the time and space complexity of applying it can be determined in advance. For other techniques that take advantage of the structure of the constraint graphs to reduce search, see (Dechter 1988a; Dechter 1988c; Dechter 1986; Freuder 1985; Freuder 1990; Dechter 1990b; Figure 5: A tree structured constraint graph and one of its width 1 orderings Zabih 1990; Perlin 1991; Montanari 1991) 4 How Much Constraint Propagation is Useful So far we have considered two rather different schemes for solving the CSP: backtracking and ....

....depending upon the analysis done to find the culprit of failure. Recall that these schemes make no effort to avoid redundant work. A simple intelligent backtracking scheme may turn out to have less overall complexity than a more complicated intelligent backtracking. The scheme presented in (Freuder 1985) can be viewed as a simple intelligent backtracking scheme that takes advantage of the structure of the constraint graph to determine possible culprits of failure. Intelligent backtracking schemes developed for Prolog (e.g. Kumar 1988; Bruynooghe 1984; Pereira 1982) are also applicable to the ....

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Freuder, E. and Quinn, M. 1985. Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 1076-1078. Menlo Park, Calif.: International Joint Conferences on Artificial Intelligence.

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Eugene C. Freuder and Michael J. Quinn. Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of International Joint Conference on Artificial Intellgence, pages 1076--1078, 1985.

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Freuder, E.C. and Quinn, M.J. 1985. Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of IJCAI-85.

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Freuder, E.C., Quinn, M.J.: Taking advantage of stable sets of variables in constraint satisfaction problems. In: International Joint Conference on Artificial Intelligene. (1985) 1076--1078

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E.C. Freuder and M.J. Quinn. Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of the International Joint Conference of AI, 1985.

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E.C. Freuder and M.J. Quinn. Taking advantage of stable sets of variables in constraint satisfaction problems. In Proceedings of the International Joint Conference of AI, 1985.

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Freuder, E.C. and Quinn, M.J., Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems, In Proceedings of IJCAI-85, 1076-1078, 1985.

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Freuder, E. and Quinn, M. "Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems", Proceedings of the Ninth International Joint Conference on Artificial Intelligence, 1076-1078. Menlo Park, Calif. (1985)

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