Artificial Intelligence最新文献

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

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-13 DOI: 10.1016/j.artint.2025.104384

Ali Zakeri, Zhuowen Zou, Hanning Chen, Mohsen Imani

引用次数: 0

Estimating possible causal effects with latent variables via adjustment and novel rule orientation 通过调整和新规则导向估计潜在变量可能的因果效应

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-13 DOI: 10.1016/j.artint.2025.104387

Tian-Zuo Wang , Lue Tao , Tian Qin , Zhi-Hua Zhou

{"title":"Estimating possible causal effects with latent variables via adjustment and novel rule orientation","authors":"Tian-Zuo Wang , Lue Tao , Tian Qin , Zhi-Hua Zhou","doi":"10.1016/j.artint.2025.104387","DOIUrl":"10.1016/j.artint.2025.104387","url":null,"abstract":"<div><div>Causal effect estimation from observational data is a fundamental task in artificial intelligence and has been widely studied given known causal relations. However, in the presence of latent confounders, only a part of causal relations can be identified from observational data, characterized by a partial ancestral graph (PAG), where some causal relations are indeterminate. In such cases, the causal effect is often unidentifiable, as there could be super-exponential number of potential causal graphs consistent with the identified PAG but associated with different causal effects. In this paper, we target on <em>set determination</em> within a PAG, <em>i.e.</em>, determining the set of possible causal effects of a specified variable <em>X</em> on another variable <em>Y</em> via covariate adjustment. We develop the first set determination method that does not require enumerating any causal graphs. Furthermore, we present two novel orientation rules for incorporating structural background knowledge (BK) into a PAG, which facilitate the identification of additional causal relations given BK. Notably, we show that these rules can further enhance the efficiency of our set determination method, as certain transformed edges during the procedure can be interpreted as BK and enable the rules to reveal further causal information. Theoretically and empirically, we demonstrate that our set determination methods can yield the same results as the enumeration-based method with <em>super-exponentially less</em> computational complexity.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104387"},"PeriodicalIF":5.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298811","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

Adversarially robust unsupervised domain adaptation 对抗鲁棒无监督域自适应

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-13 DOI: 10.1016/j.artint.2025.104383

Lianghe Shi, Weiwei Liu

{"title":"Adversarially robust unsupervised domain adaptation","authors":"Lianghe Shi, Weiwei Liu","doi":"10.1016/j.artint.2025.104383","DOIUrl":"10.1016/j.artint.2025.104383","url":null,"abstract":"<div><div>Unsupervised domain adaptation (UDA) has been successfully applied in many contexts with domain shifts. However, we find that existing UDA methods are vulnerable to adversarial attacks. A direct modification of the existing UDA methods to improve adversarial robustness is to feed the algorithms with adversarial source examples. However, empirical results show that traditional discrepancy fails to measure the distance between adversarial examples, leading to poor alignment between adversarial examples of source and target domains and inefficient transfer of the robustness from source domain to target domain. And the traditional theoretical bounds do not always hold in adversarial scenarios. Accordingly, we first propose a novel adversarial discrepancy (AD) to narrow the gap between adversarial robustness and UDA. Based on AD, this paper provides a generalization error bound for adversarially robust unsupervised domain adaptation through the lens of Rademacher complexity, theoretically demonstrating that the expected adversarial target error can be bounded by empirical adversarial source error and AD. We also present the upper bounds of Rademacher complexity, with a particular focus on linear models and multi-layer neural networks under <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>r</mi></mrow></msub></math></span> attack (<span><math><mi>r</mi><mo>≥</mo><mn>1</mn></math></span>). Inspired by this theory, we go on to develop an adversarially robust algorithm for UDA. We further conduct comprehensive experiments to support our theory and validate the robustness improvement of our proposed method on challenging domain adaptation tasks.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104383"},"PeriodicalIF":5.1,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305110","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