分模块估值的高效率和有效的预算可行机制

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kai Han , Haotian Zhang , Shuang Cui
{"title":"分模块估值的高效率和有效的预算可行机制","authors":"Kai Han ,&nbsp;Haotian Zhang ,&nbsp;Shuang Cui","doi":"10.1016/j.artint.2025.104348","DOIUrl":null,"url":null,"abstract":"<div><div>We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for submodular valuation functions, which has been extensively studied since the seminal paper of Singer [FOCS'10] due to their wide applications in crowdsourcing and social marketing. We propose <span><math><mi>TripleEagle</mi></math></span>, a novel algorithmic framework for designing BFMs, based on which we present several simple yet effective BFMs that achieve better approximation ratios than the state-of-the-art work. Moreover, our BFMs are the first in the literature to achieve linear query complexity under the value oracle model while ensuring obvious strategyproofness, making them more practical than the previous BFMs. We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results demonstrate the superiorities of our approach in terms of efficiency and effectiveness compared to the state-of-the-art BFMs.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"345 ","pages":"Article 104348"},"PeriodicalIF":4.6000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient and effective budget-feasible mechanisms for submodular valuations\",\"authors\":\"Kai Han ,&nbsp;Haotian Zhang ,&nbsp;Shuang Cui\",\"doi\":\"10.1016/j.artint.2025.104348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for submodular valuation functions, which has been extensively studied since the seminal paper of Singer [FOCS'10] due to their wide applications in crowdsourcing and social marketing. We propose <span><math><mi>TripleEagle</mi></math></span>, a novel algorithmic framework for designing BFMs, based on which we present several simple yet effective BFMs that achieve better approximation ratios than the state-of-the-art work. Moreover, our BFMs are the first in the literature to achieve linear query complexity under the value oracle model while ensuring obvious strategyproofness, making them more practical than the previous BFMs. We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results demonstrate the superiorities of our approach in terms of efficiency and effectiveness compared to the state-of-the-art BFMs.</div></div>\",\"PeriodicalId\":8434,\"journal\":{\"name\":\"Artificial Intelligence\",\"volume\":\"345 \",\"pages\":\"Article 104348\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0004370225000670\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0004370225000670","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

我们重新审视为子模块估值函数设计预算可行机制(BFMs)的经典问题,自Singer的开创性论文[FOCS'10]以来,由于其在众包和社会营销中的广泛应用,该问题得到了广泛研究。我们提出了TripleEagle,一个设计bfm的新算法框架,在此基础上,我们提出了几个简单而有效的bfm,它们比最先进的工作实现了更好的近似比。此外,我们的bfm是文献中第一个在值oracle模型下实现线性查询复杂度的,同时保证了明显的策略证明性,使它们比以前的bfm更实用。我们进行了大量的实验来评估我们的bfm的经验性能,实验结果表明,与最先进的bfm相比,我们的方法在效率和有效性方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient and effective budget-feasible mechanisms for submodular valuations
We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for submodular valuation functions, which has been extensively studied since the seminal paper of Singer [FOCS'10] due to their wide applications in crowdsourcing and social marketing. We propose TripleEagle, a novel algorithmic framework for designing BFMs, based on which we present several simple yet effective BFMs that achieve better approximation ratios than the state-of-the-art work. Moreover, our BFMs are the first in the literature to achieve linear query complexity under the value oracle model while ensuring obvious strategyproofness, making them more practical than the previous BFMs. We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results demonstrate the superiorities of our approach in terms of efficiency and effectiveness compared to the state-of-the-art BFMs.
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
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信