Artificial Intelligence最新文献

英文中文

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

Xiaowei Wu, Cong Zhang, Shengwei Zhou

We study how to fairly allocate a set of indivisible chores to a group of agents, where each agent i has a non-negative weight

w_{i}

that represents her obligation for undertaking the chores. We consider the fairness notion of weighted envy-freeness up to one item (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(

1, 1

). 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.

我们研究如何公平地将一组不可分割的杂务分配给一组智能体，其中每个智能体i有一个非负的权重wi，表示她承担杂务的义务。考虑了加权嫉妒自由度（WEF1）的公平性概念，提出了一种高效的WEF1分配算法。我们的分析是基于对加权设置中挑选序列算法的自然和强大的连续解释，这可能是独立的兴趣。利用这一解释，我们建立了选择序列算法在加权设置下保证其他公平性概念的充分必要条件。我们还研究了两全其美的设置，并提出了一个彩票，保证事前和事后的世界经济论坛（1,1）。然后，我们研究了公平和有效分配的存在性，并提出了计算双值实例的WEF1和PO分配的有效算法。我们的结果将Garg等人（AAAI 2022）和Ebadian等人（AAMAS 2022）的结果推广到加权设置。我们的工作还研究了WEF1的公平价格，以及WEF1对其他公平概念的影响。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

Pasin Manurangsi , Warut Suksompong

Fairness and privacy are two important concerns in social decision-making processes such as resource allocation. We initiate the study of privacy in fair division by investigating the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. On the other hand, we provide strong negative results for both fairness criteria when the adjacency notion allows the entire utility function of a single agent to change.

公平和隐私是资源分配等社会决策过程中的两个重要问题。本文通过对不可分割资源的公平分配问题的研究，利用已建立的差异隐私框架，开启了对公平分配中的隐私问题的研究。我们提出了近似嫉妒自由和比例性的算法，当两个实例被认为是相邻的，如果它们只是在单个代理对单个项目的效用上不同。另一方面，当邻接概念允许单个代理的整个效用函数改变时，我们为两个公平标准提供了强有力的否定结果。

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

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

We study the convergence of Q-learning-based methods in convergently non-stationary environments, particularly in the context of hierarchical reinforcement learning and of dynamic features encountered in deep reinforcement learning. We demonstrate that Q-learning achieves convergence in tabular representations when applied to convergently non-stationary dynamics, such as the ones arising in a feudal hierarchical setting. Additionally, we establish convergence for Q-learning-based deep reinforcement learning methods with convergently non-stationary features, such as the ones arising in representation-based settings. Our findings offer theoretical support for the application of Q-learning in these complex scenarios and present methodologies for extending established theoretical results from standard cases to their convergently non-stationary counterparts.

我们研究了基于q学习的方法在收敛非平稳环境中的收敛性，特别是在分层强化学习和深度强化学习中遇到的动态特征的背景下。我们证明，当应用于收敛的非平稳动态时，q学习在表格表示中实现收敛，例如在封建等级设置中产生的动态。此外，我们建立了基于q学习的深度强化学习方法的收敛性，该方法具有收敛的非平稳特征，例如基于表示的设置中出现的特征。我们的研究结果为Q-learning在这些复杂场景中的应用提供了理论支持，并提出了将已建立的理论结果从标准案例扩展到收敛非平稳对应案例的方法。

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

Ali Zakeri, Zhuowen Zou, Hanning Chen, Mohsen Imani

Graph analysis has emerged as a crucial field, offering versatile solutions for real-world data representation, from social networks to biological systems. However, the intricate nature of graphs often necessitates a degree of processing, such as learning mappings to a vector space, to perform analysis tasks like node classification and link prediction. A promising approach to this is Hyperdimensional Computing (HDC), inspired by neuroscience and mathematics. HDC utilizes high-dimensional vectors to efficiently manipulate complex data structures and perform operations like superposition and association, enhancing knowledge graph representations with contextual and semantic information. Nevertheless, addressing limitations in existing HDC-based approaches to graph representation is essential. This paper thoroughly explores these methods and presents ConfiGR: Configurable Graph Representation, a novel framework that introduces an adjustable design, enhancing its versatility across various graph types and tasks, ultimately boosting performance in multiple graph-related tasks.

图形分析已经成为一个重要的领域，为现实世界的数据表示提供了多种解决方案，从社会网络到生物系统。然而，图的复杂性质通常需要一定程度的处理，例如学习到向量空间的映射，以执行节点分类和链接预测等分析任务。超维计算（HDC）是一种很有前途的方法，它受到神经科学和数学的启发。HDC利用高维向量有效地处理复杂的数据结构，并执行叠加和关联等操作，增强知识图的上下文和语义信息表示。然而，解决现有基于hdc的图形表示方法的局限性是必不可少的。本文深入探讨了这些方法，并提出了ConfiGR：可配置图形表示，这是一个引入可调设计的新框架，增强了其在各种图形类型和任务中的多功能性，最终提高了多个图形相关任务的性能。

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

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

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 set determination within a PAG, i.e., determining the set of possible causal effects of a specified variable X on another variable Y 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 super-exponentially less computational complexity.

从观测数据中估计因果效应是人工智能的一项基本任务，在已知因果关系的情况下已经得到了广泛的研究。然而，在潜在混杂因素的存在下，只有一部分因果关系可以从观测数据中识别出来，其特征是部分祖先图（PAG），其中一些因果关系是不确定的。在这种情况下，因果效应通常是无法识别的，因为可能有超指数数量的潜在因果图与已识别的PAG一致，但与不同的因果效应相关。在本文中，我们的目标是在PAG内确定集合，即通过协变量调整确定指定变量X对另一个变量Y的可能因果效应集。我们开发了不需要列举任何因果图的第一个集合确定方法。此外，我们提出了将结构背景知识（BK）纳入PAG的两个新的取向规则，这有助于识别给定BK的附加因果关系。值得注意的是，我们表明这些规则可以进一步提高我们的集合确定方法的效率，因为在过程中某些转换的边可以被解释为BK，并使规则能够揭示进一步的因果信息。从理论上和经验上，我们证明了我们的集合确定方法可以产生与基于枚举的方法相同的结果，并且计算复杂度低于指数级。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

Lianghe Shi, Weiwei Liu

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

ℓ_{r}

attack (

r \geq 1

). 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.

无监督域自适应（UDA）已成功地应用于许多具有域漂移的环境中。然而，我们发现现有的UDA方法容易受到对抗性攻击。为了提高对抗鲁棒性，对现有UDA方法的直接修改是为算法提供对抗源示例。然而，实证结果表明，传统的差异方法无法衡量对抗示例之间的距离，导致源域和目标域的对抗示例之间的一致性较差，并且鲁棒性从源域到目标域的转移效率低下。传统的理论界限并不总是适用于对抗的情况。因此，我们首先提出了一种新的对抗差异（AD）来缩小对抗鲁棒性和UDA之间的差距。基于AD，通过Rademacher复杂度给出了对抗鲁棒无监督域自适应的泛化误差界，从理论上证明了期望的对抗目标误差可以由经验对抗源误差和AD定界。我们还给出了Rademacher复杂度的上界，特别关注了线性模型和多层神经网络在r≥1攻击下的问题。受这一理论的启发，我们继续为UDA开发一种对抗鲁棒算法。我们进一步进行了全面的实验来支持我们的理论，并验证了我们提出的方法在具有挑战性的领域适应任务中的鲁棒性改进。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

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

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 n, 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

O (k n^{2} c)

, where k is the number of agents and c the maximum length of the corridors of the graph.

在给定图上，多智能体寻路（MAPF）问题包括为多个智能体寻找路径，避免碰撞。已知找到最小长度的解是np困难的，并且计算时间随着代理的数量呈指数增长。然而，在工业应用中，重要的是要找到可行的，次优的解决方案，在一个多项式增长的时间与代理的数量。这种算法存在于无向图和双连通有向图。我们的主要贡献是将这些算法推广到更一般的强连通有向图的情况。特别地，我们描述了一个在线性时间内根据顶点数n检验问题可行性的过程，并找到了任意MAPF实例的可行性的充分必要条件。此外，我们提出了一种算法（diSC），它提供了一个长度为O（kn2c）的可行解，其中k为智能体的数量，c为图的走廊的最大长度。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

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

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, F-UCB, 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, F-Track, 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 F-SR, a nearly optimal rejection-based algorithm for identifying the best action vector, given a time budget.²

在一些现实世界的顺序决策问题中，在每一步，学习者都需要选择不同的动作。每个动作都会影响系统的特定部分，并产生可观察到的中间效应。在本文中，我们引入了因子奖励强盗（frb），这是一种能够有效捕获和利用这类场景结构的新设置，其中奖励是作为行动中间观察的产物计算的。我们通过推导最坏情况和渐近实例依赖的遗憾下界来表征frb中学习问题的统计复杂性。然后，我们设计并分析了两种遗憾最小化算法。前者，F-UCB，是一种随时乐观的方法，匹配最坏情况下界（直到对数因子），但从依赖实例的角度来看，它不能达到最佳效果。后者，F-Track，是一种边界跟踪方法，具有最优的渐近依赖实例的后悔保证。最后，我们研究了在这种情况下进行最佳手臂识别的问题。我们推导了错误概率下界，并开发了F-SR，这是一种基于几乎最优拒绝的算法，用于在给定时间预算的情况下识别最佳动作向量

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

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

Few-shot domain adaptation (FDA) aims to train a target model with clean labeled data from the source domain and few 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 wildly few-shot domain adaptation (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 noise-tolerant few-shot adaptation network (NT-FAN). Experiments demonstrate that our method significantly outperforms all the state-of-the-art competitors while maintaining a more lightweight 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.

少射域自适应（few -shot domain adaptation， FDA）的目的是用源域的清晰标记数据和目标域的少量标记数据训练目标模型。给定有限的注释预算，源数据可能包含许多嘈杂的标签，这可能会对实际应用程序中的模型性能产生不利影响。这个问题设置被表示为广泛少射域自适应（WFDA），同时照顾到标签噪声和数据短缺。虽然以前的研究取得了一些成功，但它们通常依赖于多个自适应模型来协同过滤噪声标签，导致大量的计算开销。为了更简单和优雅地解决WFDA问题，我们对该问题进行了理论分析，并提出了目标域上超额风险的综合上界。我们的理论结果表明，即使在存在源噪声和有限目标数据的情况下，也可以获得正确的域不变表示，而不会产生额外的成本。为此，我们提出了一种简单而有效的WFDA方法，称为耐噪少射自适应网络（NT-FAN）。实验表明，我们的方法在保持更轻量级架构的同时，显著优于所有最先进的竞争对手。值得注意的是，NT-FAN在处理源域中更现实和棘手的源噪声（例如，实例相关的标签噪声）和严重的源噪声（例如，40%的噪声率）时始终表现出稳健的性能。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

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

Marco Alberti , Evelina Lamma , Fabrizio Riguzzi , Riccardo Zese

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^FS. Because the grounding of a PHKB^FS can be infinite, we propose a novel semantics which does not require the PHKB^FS's grounding to be finite. We show that the proposed semantics extends the previously proposed semantics and that, for a large class of PHKB^FS, every query can be assigned a probability.

混合知识库以否定为失效语义，成功地集成了最小知识下的逻辑规划（LP）和描述逻辑（DL）。两个世界关闭假设（开放和封闭）都可以在同一个HKB中使用，这是许多领域（如法律和保健领域）所需的功能。在之前的工作中，我们提出了（无函数的）概率HKB，其语义将Sato的分布语义方法应用于Knorr等人以及Lyu和You提出的有充分根据的HKB语义。这种语义依赖于这样一个事实：无函数概率HKB （PHKB）的基础是有限的。在本文中，我们扩展PHKB语言以允许函数符号，从而获得PHKBFS。由于PHKBFS的基础可以是无限的，我们提出了一种新的语义，它不要求PHKBFS的基础是有限的。我们展示了建议的语义扩展了之前提出的语义，并且对于一个大的PHKBFS类，每个查询都可以分配一个概率。

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