文章基本信息

标题：Encoding Selection for Solving Hamiltonian Cycle Problems with ASP
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作者：Liu Liu ; Miroslaw Truszczynski
期刊名称：Electronic Proceedings in Theoretical Computer Science
电子版ISSN：2075-2180
出版年度：2019
卷号：306
页码：302-308
DOI：10.4204/EPTCS.306.35
语种：English
出版社：Open Publishing Association
摘要：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.