Automatically generating streamlined constraint models with ESSENCE and CONJURE
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Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously, effective streamlined models have been constructed by hand, requiring an expert to examine closely solutions to small instances of a problem class and identify regularities. We present a system that automatically generates many conjectured regularities for a given Essence specification of a problem class by examining the domains of decision variables present in the problem specification. These conjectures are evaluated independently and in conjunction with one another on a set of instances from the specified class via an automated modelling tool-chain comprising of Conjure, Savile Row and Minion. Once the system has identified effective conjectures they are used to generate streamlined models that allow instances of much larger scale to be solved. Our results demonstrate good models can be identified for problems in combinatorial design, Ramsey theory, graph theory and group theory - often resulting in order of magnitude speed-ups.
Wetter , J , Akgun , O & Miguel , I 2015 , Automatically generating streamlined constraint models with ESSENCE and CONJURE . in G Pesant (ed.) , Principles and Practice of Constraint Programming : 21st International Conference, CP 2015, Cork, Ireland, August 31 -- September 4, 2015, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 9255 (LNCS) , Springer , pp. 480-496 , 21st International Conference on the Principles and Practice of Constraint Programming, CP 2015 , Cork , Ireland , 31/08/15 . https://doi.org/10.1007/978-3-319-23219-5_34conference
Principles and Practice of Constraint Programming
Copyright © 2015, Springer International Publishing. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1007/978-3-319-23219-5_34