问题和解决者的基准:一种博弈论方法

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Foundations of Computing and Decision Sciences Pub Date : 2019-06-01 DOI:10.2478/fcds-2019-0008
Joseph Gogodze
{"title":"问题和解决者的基准:一种博弈论方法","authors":"Joseph Gogodze","doi":"10.2478/fcds-2019-0008","DOIUrl":null,"url":null,"abstract":"Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.","PeriodicalId":42909,"journal":{"name":"Foundations of Computing and Decision Sciences","volume":"44 1","pages":"137 - 150"},"PeriodicalIF":1.8000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Benchmarking of Problems and Solvers: a Game-Theoretic Approach\",\"authors\":\"Joseph Gogodze\",\"doi\":\"10.2478/fcds-2019-0008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.\",\"PeriodicalId\":42909,\"journal\":{\"name\":\"Foundations of Computing and Decision Sciences\",\"volume\":\"44 1\",\"pages\":\"137 - 150\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Foundations of Computing and Decision Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/fcds-2019-0008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations of Computing and Decision Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/fcds-2019-0008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 1

摘要

摘要在本文中,我们提出了一种博弈论方法来测试计算问题及其求解器。该方法将评估矩阵作为某个零和矩阵游戏的回报矩阵,在该游戏中,第一个玩家选择问题,第二个玩家选择求解器。该游戏的混合策略中的解决方案用于构建所考虑的问题和解决者的理论上客观的排名。通过实例说明了所提出的方法,以证明其可行性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Benchmarking of Problems and Solvers: a Game-Theoretic Approach
Abstract In this note, we propose a game-theoretic approach for benchmarking computational problems and their solvers. The approach takes an assessment matrix as a payoff matrix for some zero-sum matrix game in which the first player chooses a problem and the second player chooses a solver. The solution in mixed strategies of this game is used to construct a notionally objective ranking of the problems and solvers under consideration. The proposed approach is illustrated in terms of an example to demonstrate its viability and its suitability for applications.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Foundations of Computing and Decision Sciences
Foundations of Computing and Decision Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.20
自引率
9.10%
发文量
16
审稿时长
29 weeks
期刊最新文献
A DNA Algorithm for Calculating the Maximum Flow of a Network Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study Traveling salesman problem parallelization by solving clustered subproblems Towards automated recommendations for drunk driving penalties in Poland - a case study analysis in selected court Designing a Tri-Objective, Sustainable, Closed-Loop, and Multi-Echelon Supply Chain During the COVID-19 and Lockdowns
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1