Automatic generation and selection of streamlined constraint models via Monte Carlo search on a model lattice
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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 it has been established that it is possible to generate streamliners automatically from abstract constraint specifications in Essence and that effective combinations of streamliners can allow instances of much larger scale to be solved. A shortcoming of the previous approach was the crude exploration of the power set of all combinations using depth and breadth first search. We present a new approach based on Monte Carlo search over the lattice of streamlined models, which efficiently identifies effective streamliner combinations.
Spracklen , P , Akgun , O & Miguel , I J 2018 , Automatic generation and selection of streamlined constraint models via Monte Carlo search on a model lattice . in J Hooker (ed.) , Principles and Practice of Constraint Programming : 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings . Lecture Notes in Computer Science (including subseries Programming and Software Engineering) , vol. 11008 LNCS , Springer , Cham , pp. 362-372 . https://doi.org/10.1007/978-3-319-98334-9_24
Principles and Practice of Constraint Programming
Copyright © 2018, Springer Nature Switzerland AG. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1007/978-3-319-98334-9_24
DescriptionFunding: EPSRC EP/P015638/1.
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