Artificial Intelligence最新文献_第4页

On the design of truthful mechanisms for the capacitated facility location problem with two and more facilities 两个及两个以上可容设施选址问题的真实机制设计

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-07-07 DOI: 10.1016/j.artint.2025.104390

Gennaro Auricchio , Zihe Wang , Jie Zhang

{"title":"On the design of truthful mechanisms for the capacitated facility location problem with two and more facilities","authors":"Gennaro Auricchio , Zihe Wang , Jie Zhang","doi":"10.1016/j.artint.2025.104390","DOIUrl":"10.1016/j.artint.2025.104390","url":null,"abstract":"<div><div>In this paper, we explore the Mechanism Design aspects of the <em>m</em>-Capacitated Facility Location Problem (<em>m</em>-CFLP) on a line, focusing on two frameworks. In the first framework, the number of facilities is arbitrary, all facilities share the same capacity, and the number of agents matches the total capacity of the facilities. In the second framework, we need to locate two facilities, each with a capacity equal to at least half the number of agents. For both frameworks, we propose truthful mechanisms with bounded approximation ratios in terms of Social Cost (SC) and Maximum Cost (MC). When <span><math><mi>m</mi><mo>></mo><mn>2</mn></math></span>, our results stand in contrast to the impossibility results known for the classical <em>m</em>-Facility Location Problem, where capacity constraints are absent. Moreover, all the proposed mechanisms are optimal with respect to MC and either optimal or near-optimal with respect to the SC among anonymous mechanisms. We then establish lower bounds on the approximation ratios that any truthful and deterministic mechanism achieves with respect to SC and MC for both frameworks. Lastly, we run several numerical experiments to empirically evaluate the performances of our mechanisms with respect to the SC or the MC. Our empirical analysis shows that our proposed mechanisms outperform all previously proposed mechanisms applicable in this setting.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104390"},"PeriodicalIF":5.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Introduction to open-world AI 开放世界AI简介

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-07-04 DOI: 10.1016/j.artint.2025.104393

Lawrence Holder , Pat Langley , Bryan Loyall , Ted Senator

引用次数: 0

Regression-based conditional independence test with adaptive kernels 基于回归的自适应核条件独立性检验

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-07-01 DOI: 10.1016/j.artint.2025.104391

Yixin Ren , Juncai Zhang , Yewei Xia , Ruxin Wang , Feng Xie , Jihong Guan , Hao Zhang , Shuigeng Zhou

{"title":"Regression-based conditional independence test with adaptive kernels","authors":"Yixin Ren , Juncai Zhang , Yewei Xia , Ruxin Wang , Feng Xie , Jihong Guan , Hao Zhang , Shuigeng Zhou","doi":"10.1016/j.artint.2025.104391","DOIUrl":"10.1016/j.artint.2025.104391","url":null,"abstract":"<div><div>We propose a novel framework for regression-based conditional independence (CI) test with adaptive kernels, where the task of CI test is reduced to regression and statistical independence test while proving that the test power of CI can be maximized by adaptively learning parameterized kernels of the independence test if the consistency of regression can be guaranteed. For the adaptively learning kernel of independence test, we first address the pitfall inherent in the existing signal-to-noise ratio criterion by modeling the change of the null distribution during the learning process, then design a new class of kernels that can adaptively focus on the significant dimensions of variables to judge independence, which makes the tests more flexible than using simple kernels that are adaptive only in length-scale, and especially suitable for high-dimensional complex data. Theoretically, we demonstrate the consistency of the proposed tests, and show that the non-convex objective function used for learning fits the L-smoothing condition, thus benefiting the optimization. Experimental results on both synthetic and real data show the superiority of our method. The source code and datasets are available at <span><span>https://github.com/hzsiat/AdaRCIT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104391"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fair distribution of delivery orders 公平分配交货订单

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-27 DOI: 10.1016/j.artint.2025.104389

Hadi Hosseini , Shivika Narang , Tomasz Wąs

{"title":"Fair distribution of delivery orders","authors":"Hadi Hosseini , Shivika Narang , Tomasz Wąs","doi":"10.1016/j.artint.2025.104389","DOIUrl":"10.1016/j.artint.2025.104389","url":null,"abstract":"<div><div>We initiate the study of fair distribution of delivery tasks among a set of agents wherein delivery jobs are placed along the vertices of a graph. Our goal is to fairly distribute delivery costs (distance traveled to complete the deliveries) among a fixed set of agents while satisfying some desirable notions of economic efficiency. We adopt well-established fairness concepts—such as <em>envy-freeness up to one item</em> (EF1) and <em>minimax share</em> (MMS)—to our setting and show that fairness is often incompatible with the efficiency notion of <em>social optimality</em>. We then characterize instances that admit fair and socially optimal solutions by exploiting graph structures. We further show that achieving fairness along with Pareto optimality is computationally intractable. We complement this by designing an XP algorithm (parameterized by the number of agents) for finding MMS and Pareto optimal solutions on every tree instance, and show that the same algorithm can be modified to find efficient solutions along with EF1, when such solutions exist. The latter crucially relies on an intriguing result that in our setting EF1 and Pareto optimality jointly imply MMS. We conclude by theoretically and experimentally analyzing the price of fairness.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104389"},"PeriodicalIF":5.1,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144516138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scalable multi-robot goal assignment algorithm for minimizing mission time followed by total movement cost 最小化任务时间和总运动成本的可扩展多机器人目标分配算法

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.artint.2025.104388

Aakash, Indranil Saha

{"title":"A scalable multi-robot goal assignment algorithm for minimizing mission time followed by total movement cost","authors":"Aakash, Indranil Saha","doi":"10.1016/j.artint.2025.104388","DOIUrl":"10.1016/j.artint.2025.104388","url":null,"abstract":"<div><div>We study a variant of the multi-robot goal assignment problem where a unique goal for each robot needs to be assigned while minimizing the largest cost of movement among the robots, called makespan, and then minimizing the total movement cost of all the robots without exceeding the optimal makespan. A significant step in solving this problem is to find the cost associated with each robot-goal pair, which requires solving several complex path planning problems, thus, limiting the scalability. We present an algorithm that solves the multi-robot goal assignment problem by computing the paths for a significantly smaller number of robot-goal pairs compared to state-of-the-art algorithms, leading to a computationally superior mechanism to solve the problem. We perform theoretical analysis to establish the correctness and optimality of the proposed algorithm, as well as its worst-case polynomial time complexity. We extensively evaluate our algorithm for hundreds of robots on randomly generated and standard workspaces. Our experimental results demonstrate that the proposed algorithm achieves a noticeable speedup over two state-of-the-art baseline algorithms.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104388"},"PeriodicalIF":5.1,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approval-based committee voting under incomplete information 基于批准的委员会在不完整信息下的投票

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.artint.2025.104381

Aviram Imber , Jonas Israel , Markus Brill , Benny Kimelfeld

引用次数: 0

Weighted EF1 allocations for indivisible chores 不可分割杂务的加权EF1分配

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-16 DOI: 10.1016/j.artint.2025.104386

Xiaowei Wu, Cong Zhang, Shengwei Zhou

{"title":"Weighted EF1 allocations for indivisible chores","authors":"Xiaowei Wu, Cong Zhang, Shengwei Zhou","doi":"10.1016/j.artint.2025.104386","DOIUrl":"10.1016/j.artint.2025.104386","url":null,"abstract":"<div><div>We study how to fairly allocate a set of indivisible chores to a group of agents, where each agent <em>i</em> has a non-negative weight <span><math><msub><mrow><mi>w</mi></mrow><mrow><mi>i</mi></mrow></msub></math></span> that represents her obligation for undertaking the chores. We consider the fairness notion of <em>weighted envy-freeness up to one item</em> (WEF1) and propose an efficient picking sequence algorithm for computing WEF1 allocations. Our analysis is based on a natural and powerful continuous interpretation for the picking sequence algorithms in the weighted setting, which might be of independent interest. Using this interpretation, we establish the necessary and sufficient conditions under which picking sequence algorithms can guarantee other fairness notions in the weighted setting. We also study the best-of-both-worlds setting and propose a lottery that guarantees ex-ante WEF and ex-post WEF(<span><math><mn>1</mn><mo>,</mo><mn>1</mn></math></span>). Then we study the existence of fair and efficient allocations and propose efficient algorithms for computing WEF1 and PO allocations for bi-valued instances. Our result generalizes that of Garg et al. (AAAI 2022) and Ebadian et al. (AAMAS 2022) to the weighted setting. Our work also studies the price of fairness for WEF1, and the implications of WEF1 to other fairness notions.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104386"},"PeriodicalIF":5.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentially private fair division 差别私人公平划分

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-13 DOI: 10.1016/j.artint.2025.104385

Pasin Manurangsi , Warut Suksompong

引用次数: 0

Reinforcement learning in convergently non-stationary environments: Feudal hierarchies and learned representations 收敛非平稳环境中的强化学习：封建等级和学习表征

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-13 DOI: 10.1016/j.artint.2025.104382

Diogo S. Carvalho, Pedro A. Santos, Francisco S. Melo

引用次数: 0

Configurable hyperdimensional graph representation 可配置的超维图表示