Artificial Intelligence最新文献_第2页

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

Multi-agent pathfinding on strongly connected digraphs: Feasibility and solution algorithms 强连接有向图上的多智能体寻路：可行性和求解算法

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-06-04 DOI: 10.1016/j.artint.2025.104372

S. Ardizzoni , L. Consolini , M. Locatelli , B. Nebel , I. Saccani

{"title":"Multi-agent pathfinding on strongly connected digraphs: Feasibility and solution algorithms","authors":"S. Ardizzoni , L. Consolini , M. Locatelli , B. Nebel , I. Saccani","doi":"10.1016/j.artint.2025.104372","DOIUrl":"10.1016/j.artint.2025.104372","url":null,"abstract":"<div><div>On an assigned graph, the problem of Multi-Agent Pathfinding (MAPF) consists in finding paths for multiple agents, avoiding collisions. Finding the minimum-length solution is known to be NP-hard, and computation times grows exponentially with the number of agents. However, in industrial applications, it is important to find feasible, suboptimal solutions, in a time that grows polynomially with the number of agents. Such algorithms exist for undirected and biconnected directed graphs. Our main contribution is to generalize these algorithms to the more general case of strongly connected directed graphs. In particular, we describe a procedure that checks the problem feasibility in linear time with respect to the number of vertices <em>n</em>, and we find a necessary and sufficient condition for feasibility of any MAPF instance. Moreover, we present an algorithm (diSC) that provides a feasible solution of length <span><math><mi>O</mi><mo>(</mo><mi>k</mi><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mi>c</mi><mo>)</mo></math></span>, where <em>k</em> is the number of agents and <em>c</em> the maximum length of the corridors of the graph.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104372"},"PeriodicalIF":5.1,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241420","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

Factored-reward bandits with intermediate observations: Regret minimization and best arm identification 具有中间观察的因子奖励盗匪：后悔最小化和最佳武器识别

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-05-23 DOI: 10.1016/j.artint.2025.104362

Marco Mussi , Simone Drago , Marcello Restelli, Alberto Maria Metelli

{"title":"Factored-reward bandits with intermediate observations: Regret minimization and best arm identification","authors":"Marco Mussi , Simone Drago , Marcello Restelli, Alberto Maria Metelli","doi":"10.1016/j.artint.2025.104362","DOIUrl":"10.1016/j.artint.2025.104362","url":null,"abstract":"<div><div>In several real-world sequential decision problems, at every step, the learner is required to select different actions. Every action affects a specific part of the system and generates an observable intermediate effect. In this paper, we introduce the Factored-Reward Bandits (FRBs), a novel setting able to effectively capture and exploit the structure of this class of scenarios, where the reward is computed as the product of the action intermediate observations. We characterize the statistical complexity of the learning problem in the FRBs, by deriving worst-case and asymptotic instance-dependent regret lower bounds. Then, we devise and analyze two regret minimization algorithms. The former, <span>F-UCB</span>, is an anytime optimistic approach matching the worst-case lower bound (up to logarithmic factors) but fails to perform optimally from the instance-dependent perspective. The latter, <span>F-Track</span>, is a bound-tracking approach, that enjoys optimal asymptotic instance-dependent regret guarantees. Finally, we study the problem of performing best arm identification in this setting. We derive an error probability lower bound, and we develop <span>F-SR</span>, a nearly optimal rejection-based algorithm for identifying the best action vector, given a time budget.<span><span><sup>2</sup></span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104362"},"PeriodicalIF":5.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263063","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

NT-FAN: A simple yet effective noise-tolerant few-shot adaptation network NT-FAN：一种简单而有效的耐噪少射自适应网络

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-05-22 DOI: 10.1016/j.artint.2025.104363

Wenjing Yang , Haoang Chi , Yibing Zhan , Bowen Hu , Xiaoguang Ren , Dapeng Tao , Long Lan

{"title":"NT-FAN: A simple yet effective noise-tolerant few-shot adaptation network","authors":"Wenjing Yang , Haoang Chi , Yibing Zhan , Bowen Hu , Xiaoguang Ren , Dapeng Tao , Long Lan","doi":"10.1016/j.artint.2025.104363","DOIUrl":"10.1016/j.artint.2025.104363","url":null,"abstract":"<div><div><em>Few-shot domain adaptation</em> (FDA) aims to train a target model with <em>clean</em> labeled data from the source domain and <em>few</em> labeled data from the target domain. Given a limited annotation budget, source data may contain many noisy labels, which can detrimentally impact the performance of models in real-world applications. This problem setting is denoted as <em>wildly few-shot domain adaptation</em> (WFDA), simultaneously taking care of label noise and data shortage. While previous studies have achieved some success, they typically rely on multiple adaptation models to collaboratively filter noisy labels, resulting in substantial computational overhead. To address WFDA more simply and elegantly, we offer a theoretical analysis of this problem and propose a comprehensive upper bound for the excess risk on the target domain. Our theoretical result reveals that correct domain-invariant representations can be obtained even in the presence of source noise and limited target data without incurring additional costs. In response, we propose a simple yet effective WFDA method, referred to as <em>noise-tolerant few-shot adaptation network</em> (NT-FAN). Experiments demonstrate that our method significantly outperforms all the state-of-the-art competitors while maintaining a more <em>lightweight</em> architecture. Notably, NT-FAN consistently exhibits robust performance when dealing with more realistic and intractable source noise (e.g., instance-dependent label noise) and severe source noise (e.g., a 40% noise rate) in the source domain.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104363"},"PeriodicalIF":5.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139326","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 semantics for probabilistic hybrid knowledge bases with function symbols 带有函数符号的概率混合知识库的语义

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-05-20 DOI: 10.1016/j.artint.2025.104361

Marco Alberti , Evelina Lamma , Fabrizio Riguzzi , Riccardo Zese

{"title":"A semantics for probabilistic hybrid knowledge bases with function symbols","authors":"Marco Alberti , Evelina Lamma , Fabrizio Riguzzi , Riccardo Zese","doi":"10.1016/j.artint.2025.104361","DOIUrl":"10.1016/j.artint.2025.104361","url":null,"abstract":"<div><div>Hybrid Knowledge Bases (HKBs) successfully integrate Logic Programming (LP) and Description Logics (DL) under the Minimal Knowledge with Negation as Failure semantics. Both world closure assumptions (open and closed) can be used in the same HKB, a feature required in many domains, such as the legal and health-care ones. In previous work, we proposed (function-free) Probabilistic HKBs, whose semantics applied Sato's distribution semantics approach to the well-founded HKB semantics proposed by Knorr et al. and Lyu and You. This semantics relied on the fact that the grounding of a function-free Probabilistic HKB (PHKB) is finite. In this article, we extend the PHKB language to allow function symbols, obtaining PHKB<sup><em>FS</em></sup>. Because the grounding of a PHKB<sup><em>FS</em></sup> can be infinite, we propose a novel semantics which does not require the PHKB<sup><em>FS</em></sup>'s grounding to be finite. We show that the proposed semantics extends the previously proposed semantics and that, for a large class of PHKB<sup><em>FS</em></sup>, every query can be assigned a probability.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104361"},"PeriodicalIF":5.1,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098911","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