An empirical study of dynamic variable ordering heuristics for the constraint satisfaction problem

Author(s):  
Ian P. Gent ◽  
Ewan MacIntyre ◽  
Patrick Presser ◽  
Barbara M. Smith ◽  
Toby Walsh
2019 ◽  
Vol 22 (63) ◽  
pp. 1-15
Author(s):  
James L. Cox ◽  
Stephen Lucci ◽  
Tayfun Pay

We carry out a detailed analysis of the effects of different dynamic variable and value ordering heuristics on the search space of Sudoku when the encoding method and the filtering algorithm are fixed. Our study starts by examining lexicographical variable and value ordering and evaluates different combinations of dynamic variable and value ordering heuristics. We eventually build up to a dynamic variable ordering heuristic that has two rounds of tie-breakers, where the second tie-breaker is a dynamic value ordering heuristic. We show that our method that uses this interlinked heuristic outperforms the previously studied ones with the same experimental setup. Overall, we conclude that constructing insightful dynamic variable ordering heuristics that also utilize a dynamic value ordering heuristic in their decision making process could drastically improve the search effort for some constraint satisfaction problems.


2000 ◽  
Vol 12 ◽  
pp. 1-34 ◽  
Author(s):  
S. Kambhampati

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan.


2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
José Carlos Ortiz-Bayliss ◽  
Ivan Amaya ◽  
Santiago Enrique Conant-Pablos ◽  
Hugo Terashima-Marín

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.


2015 ◽  
Vol 22 (2) ◽  
pp. 125-145 ◽  
Author(s):  
Hongbo Li ◽  
Yanchun Liang ◽  
Ning Zhang ◽  
Jinsong Guo ◽  
Dong Xu ◽  
...  

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