Artificial Intelligence最新文献_第3页

Interpreting capsule networks for image classification by routing path visualization 基于路由路径可视化的图像分类胶囊网络解释

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-07-17 DOI: 10.1016/j.artint.2025.104395

Amanjot Bhullar , Michael Czomko , R. Ayesha Ali , Douglas L. Welch

引用次数: 0

Relaxed core stability in hedonic games 在享乐游戏中放松核心稳定性

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-07-09 DOI: 10.1016/j.artint.2025.104394

Angelo Fanelli , Gianpiero Monaco , Luca Moscardelli

{"title":"Relaxed core stability in hedonic games","authors":"Angelo Fanelli , Gianpiero Monaco , Luca Moscardelli","doi":"10.1016/j.artint.2025.104394","DOIUrl":"10.1016/j.artint.2025.104394","url":null,"abstract":"<div><div>The <em>core</em> is a well-known and fundamental notion of stability in games intended to model coalition formation such as hedonic games: an outcome is core stable if there exists no <em>blocking coalition</em>, i.e., no set of agents that may profit by forming a coalition together. The fact that the cardinality of a blocking coalition, i.e., the number of deviating agents that have to coordinate themselves, can be arbitrarily high, and the fact that agents may benefit only by a tiny amount from their deviation, while they could incur in a higher cost for deviating, suggest that the core is not able to suitably model practical scenarios in large and highly distributed multi-agent systems. For this reason, we consider relaxed core stable outcomes where the notion of permissible deviations is modified along two orthogonal directions: the former takes into account the size <em>q</em> of the deviating coalition, and the latter the amount of utility gain, in terms of a multiplicative factor <em>k</em>, for each member of the deviating coalition. These changes result in two different notions of stability, namely, the <em>q-size core</em> and <em>k-improvement core</em>. We consider fractional hedonic games, that is a well-known subclass of hedonic games for which core stable outcomes are not guaranteed to exist and it is computationally hard to decide non-emptiness of the core; we investigate these relaxed concepts of stability with respect to their existence, computability and performance in terms of price of anarchy and price of stability, by providing in many cases tight or almost tight bounds. Interestingly, the considered relaxed notions of core also possess the appealing property of recovering, in some notable cases, the convergence, the existence and the possibility of computing stable solutions in polynomial time.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104394"},"PeriodicalIF":5.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588462","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

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

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