文章基本信息

标题：sunny-as2: Enhancing SUNNY for Algorithm Selection
本地全文：下载
作者：Tong Liu ; Roberto Amadini ; Maurizio Gabbrielli 等
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2021
卷号：72
页码：1-48
DOI：10.1613/jair.1.13116
语种：English
出版社：American Association of Artificial
摘要：SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one to schedule from a portfolio of solvers a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems. In 2015 the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g. ASP QBF and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as called sunny-as2 was then submitted to the Open Algorithm Selection Challenge (OASC) in 2017 where it turned out to be the best approach for the runtime minimization of decision problems. In this work we present the technical advancements of sunny-as2 by detailing through several empirical evaluations and by providing new insights. Its current version built on the top of the preliminary version submitted to OASC is able to outperform sunny-as and other state-of-the-art AS methods including those who did not attend the challenge.
关键词：machine learning;optimization;heuristics;decision trees;k-NN