Learning when to use automatic tabulation in constraint model reformulation
Abstract
Combinatorial optimisation has numerous practical applications, such as planning, logistics, or circuit design. Problems such as these can be solved by approaches such as Boolean Satisfiability (SAT) or Constraint Programming (CP). Solver performance is affected significantly by the model chosen to represent a given problem, which has led to the study of model reformulation. One such method is tabulation: rewriting the expression of some of the model constraints in terms of a single “table” constraint. Successfully applying this process means identifying expressions amenable to trans- formation, which has typically been done manually. Recent work introduced an automatic tabulation using a set of hand-designed heuristics to identify constraints to tabulate. However, the performance of these heuristics varies across problem classes and solvers. Recent work has shown learning techniques to be increasingly useful in the context of automatic model reformulation. The goal of this study is to understand whether it is possible to improve the performance of such heuristics, by learning a model to predict whether or not to activate them for a given instance. Experimental results suggest that a random forest classifier is the most robust choice, improving the performance of four different SAT and CP solvers.
Citation
Cena , C , Akgun , O , Kiziltan , Z , Miguel , I J , Nightingale , P & Ulrich-Oltean , F 2023 , Learning when to use automatic tabulation in constraint model reformulation . in E Elkind (ed.) , Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 : Macao, SAR . IJCAI International Joint Conference on Artificial Intelligence , vol. 2023-August , International Joint Conferences on Artificial Intelligence , pp. 1902-1910 , 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 , Macao , China , 19/08/23 . https://doi.org/10.24963/ijcai.2023/211 conference
Publication
Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
ISSN
1045-0823Type
Conference item
Description
Funding Information: We are grateful for the computational support from the University of York HPC service, Viking and the Research Computing team. This work was supported by EPSRC grants EP/R513386/1, EP/V027182/1 and EP/W001977/1 and by a scholarship from the Department of Computer Science and Engineering of the University of Bologna. Publisher Copyright: © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.Collections
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