{"title":"Solving mean-payoff games via quasi dominions","authors":"Massimo Benerecetti , Daniele Dell'Erba , Fabio Mogavero","doi":"10.1016/j.ic.2024.105151","DOIUrl":null,"url":null,"abstract":"<div><p>We propose a novel algorithm for the solution of <em>mean-payoff games</em> that merges together two seemingly unrelated concepts introduced in the context of parity games, namely <em>small progress measures</em> and <em>quasi dominions</em>. We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.</p></div>","PeriodicalId":54985,"journal":{"name":"Information and Computation","volume":"297 ","pages":"Article 105151"},"PeriodicalIF":0.8000,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0890540124000166/pdfft?md5=306a43f9ff3894332e824e402752e4b7&pid=1-s2.0-S0890540124000166-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0890540124000166","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a novel algorithm for the solution of mean-payoff games that merges together two seemingly unrelated concepts introduced in the context of parity games, namely small progress measures and quasi dominions. We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.
期刊介绍:
Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as
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