It is common for search and optimization problems to have alternative equivalent encodings in ASP. Typically none of them is uniformly better than others when evaluated on broad classes of problem instances. We claim that one can improve the solving ability of ASP by using machine learning techniques to select encodings likely to perform well on a given instance. We substantiate this claim by studying the hamiltonian cycle problem. We propose several equivalent encodings of the problem and several classes of hard instances. We build models to predict the behavior of each encoding, and then show that selecting encodings for a given instance using the learned performance predictors leads to significant performance gains.
|Number of pages||7|
|Journal||Electronic Proceedings in Theoretical Computer Science, EPTCS|
|State||Published - Sep 19 2019|
|Event||35th International Conference on Logic Programming (Technical Communications), ICLP 2019 - Las Cruces, United States|
Duration: Sep 20 2019 → Sep 25 2019
Bibliographical noteFunding Information:
This work was funded by the NSF under the grant IIS-1707371.
© L. Liu and M. Truszczynski
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