Artificial Intelligence最新文献_第7页

MATE: Masked optimal transport with dynamic selection for partial label graph learning 部分标签图学习的动态选择掩膜最优传输

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-01 Epub Date: 2025-07-29 DOI: 10.1016/j.artint.2025.104396

Yiyang Gu , Binqi Chen , Zihao Chen , Ziyue Qiao , Xiao Luo , Junyu Luo , Zhiping Xiao , Wei Ju , Ming Zhang

This paper investigates the problem of partial label graph learning, in which every graph is associated with a set of candidate labels. Previous methods for weakly supervised graph classification often provide pseudo-labels for graph samples that could be overconfident and biased towards the dominant classes, thus resulting in substantial error accumulation. In this paper, we introduce a new framework named Masked Optimal Transport with Dynamic Selection (MATE) for partial label graph learning, which improves the quality of graph assignments from the perspectives of class balancing and uncertainty mining. In particular, our MATE masks probabilities out of candidate sets and then adopts optimal transport to optimize the assignments without class biases. This design is based on the assumption that the true label distribution is class-balanced or nearly balanced, which is common in various training datasets and real-world scenarios. To further reduce potential noise, we propose a novel scoring metric termed partial energy discrepancy (PED) to evaluate the uncertainty of assignments, and then introduce a dynamic selection strategy that modifies the sample-specific thresholds via momentum updating. Finally, these samples are divided into three levels, i.e., confident, less-confident, and unconfident and each group is trained separately in our collaborative optimization framework. Extensive experiments on various benchmarks demonstrate the superiority of our MATE compared to various state-of-the-art baselines.

本文研究了部分标签图学习问题，其中每个图都与一组候选标签相关联。以前的弱监督图分类方法通常为图样本提供伪标签，这些伪标签可能过于自信，并偏向于优势类，从而导致大量的误差积累。本文从类平衡和不确定性挖掘的角度，提出了一种新的局部标签图学习框架——动态选择掩膜最优传输（mask Optimal Transport with Dynamic Selection， MATE），提高了图分配的质量。特别地，我们的MATE屏蔽了候选集之外的概率，然后采用最优传输来优化没有类偏差的分配。这种设计基于真实标签分布是类平衡或接近平衡的假设，这在各种训练数据集和现实场景中很常见。为了进一步降低潜在的噪声，我们提出了一种新的评分指标，称为部分能量差异（PED）来评估分配的不确定性，然后引入了一种动态选择策略，通过动量更新来修改样本特定阈值。最后，将这些样本分为三个层次，即自信、不自信和不自信，并在我们的协同优化框架中单独训练每一组。在各种基准上进行的大量实验表明，与各种最先进的基线相比，我们的MATE具有优势。

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引用次数: 0

Towards optimal subsidy bounds for envy-freeable allocations 无嫉妒分配的最优补贴界限

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-01 Epub Date: 2025-08-28 DOI: 10.1016/j.artint.2025.104406

Yasushi Kawase , Kazuhisa Makino , Hanna Sumita , Akihisa Tamura , Makoto Yokoo

We study the fair division of indivisible items with subsidies among n agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. [9] demonstrated that a maximum subsidy of

2 (n - 1)

and a total subsidy of

2 {(n - 1)}^{2}

are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most

n - 1

per agent and a total subsidy of at most

n (n - 1) / 2

. Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most

n - 1.5

per agent and a total subsidy of at most

(n^{2} - n - 1) / 2

.

我们研究了n个主体对有补贴的不可分割物品的公平分配问题，其中每个物品的绝对边际价值不超过1。在单调非递减估值（其中每个项目都是好的）下，Brustle等人证明了最大补贴2（n−1）和总补贴2(n−1)2足以保证无嫉妒分配的存在。在本文中，我们改进了这些边界，甚至在更广泛的模型中。也就是说，我们证明，给定一个EF1分配，我们可以在多项式时间内计算出一个无嫉妒分配，每个代理的补贴最多为n−1，总补贴最多为n(n−1)/2。此外，当评估值为单调非递减时，我们提供了一个多项式时间算法，该算法计算每个代理的补贴最多为n−1.5，总补贴最多为(n2−n−1)/2的无嫉妒分配。

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引用次数: 0

Enhancing cooperativity in controlled query evaluation over ontologies 增强本体上受控查询计算的协同性

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-01 Epub Date: 2025-08-12 DOI: 10.1016/j.artint.2025.104402

Piero Bonatti , Gianluca Cima , Domenico Lembo , Francesco Magliocca , Lorenzo Marconi , Riccardo Rosati , Luigi Sauro , Domenico Fabio Savo

Controlled Query Evaluation (CQE) is a methodology designed to maintain confidentiality by either rejecting specific queries or adjusting responses to safeguard sensitive information. In this investigation, our focus centers on CQE within Description Logic ontologies, aiming to ensure that queries are answered truthfully as long as possible before resorting to deceptive responses, a cooperativity property which is called the “longest honeymoon”. Our work introduces new semantics for CQE, denoted as MC-CQE, which enjoys the longest honeymoon property and outperforms previous methodologies in terms of cooperativity.

We study the complexity of query answering in this new framework for ontologies expressed in the Description Logic

{DL-Lite}_{R}

. Specifically, we establish data complexity results under different maximally cooperative semantics and for different classes of queries. Our results identify both tractable and intractable cases. In particular, we show that the evaluation of Boolean unions of conjunctive queries is the same under all the above semantics and its data complexity is in

. This result makes query answering amenable to SQL query rewriting. However, this favorable property does not extend to open queries, even with a restricted query language limited to conjunctions of atoms. While, in general, answering open queries in the MC-CQE framework is intractable, we identify a sub-family of semantics under which answering full conjunctive queries is tractable.

受控查询评估（CQE）是一种旨在通过拒绝特定查询或调整响应以保护敏感信息来维护机密性的方法。在本次调查中，我们的重点是描述逻辑本体中的CQE，旨在确保在诉诸欺骗性响应之前尽可能长时间地如实回答查询，这是一种被称为“最长蜜月”的协作特性。我们的工作为CQE引入了新的语义，表示为MC-CQE，它具有最长的蜜月属性，并且在协作性方面优于以前的方法。我们研究了用描述逻辑dl - l表达的本体在这个新框架下查询回答的复杂性。具体来说，我们建立了不同最大协作语义和不同查询类别下的数据复杂度结果。我们的结果确定了易处理和难以处理的病例。特别地，我们证明了在上述所有语义下，合取查询的布尔联合的求值是相同的，其数据复杂度为。这个结果使得查询应答能够适应SQL查询重写。但是，这个有利的特性不能扩展到打开查询，即使使用限于原子连词的受限查询语言也是如此。虽然一般来说，在MC-CQE框架中回答开放查询是棘手的，但我们确定了一个子语义族，在该语义族下回答完整的连接查询是可处理的。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-11-01 Epub Date: 2025-07-09 DOI: 10.1016/j.artint.2025.104394

Angelo Fanelli , Gianpiero Monaco , Luca Moscardelli

The core 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 blocking coalition, 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 q of the deviating coalition, and the latter the amount of utility gain, in terms of a multiplicative factor k, for each member of the deviating coalition. These changes result in two different notions of stability, namely, the q-size core and k-improvement core. 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.

核心是游戏中一个众所周知的基本稳定性概念，用于模拟联盟的形成，如享乐游戏：如果不存在阻塞联盟，即没有一组代理可以通过组成联盟来获利，则结果是核心稳定的。事实上，阻塞联盟的基数，即必须协调自己的偏离代理的数量，可以任意高，并且代理可能只从他们的偏离中获得很小的收益，而他们可能会因偏离而产生更高的成本，这表明核心无法适当地模拟大型和高度分布式的多代理系统中的实际场景。出于这个原因，我们考虑宽松的核心稳定结果，其中允许偏差的概念沿着两个正交方向进行修改：前者考虑了偏离联盟的大小q，后者考虑了偏离联盟中每个成员的乘法因子k的效用增益量。这些变化导致了两种不同的稳定性概念，即q-size核心和k-improvement核心。我们考虑分数型享乐对策，这是一个众所周知的享乐对策的子类，它的核心稳定结果不能保证存在，并且计算上难以确定核心的非空性；我们通过在许多情况下提供紧或几乎紧的边界，从无政府状态的价格和稳定的价格的角度，研究了这些宽松的稳定性概念的存在性、可计算性和性能。有趣的是，所考虑的核的松弛概念也具有吸引人的性质，在一些值得注意的情况下，恢复了收敛性、存在性和在多项式时间内计算稳定解的可能性。

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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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-10-01 Epub 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 : 2025-10-01 Epub 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

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 : 2025-10-01 Epub 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

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 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-10-01 Epub Date: 2025-06-20 DOI: 10.1016/j.artint.2025.104388

Aakash, Indranil Saha

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.

本文研究了多机器人目标分配问题的一种变体，即需要为每个机器人分配一个唯一的目标，同时使机器人之间的最大运动成本最小化（称为makespan），然后在不超过最优makespan的情况下使所有机器人的总运动成本最小化。解决该问题的一个重要步骤是找到每个机器人-目标对的相关成本，这需要解决几个复杂的路径规划问题，因此限制了可扩展性。我们提出了一种算法，该算法通过计算机器人-目标对的路径来解决多机器人目标分配问题，与最先进的算法相比，它的数量要少得多，从而产生了一种计算上优越的机制来解决问题。我们进行了理论分析，以确定所提出的算法的正确性和最优性，以及它的最坏情况多项式时间复杂度。我们在随机生成和标准工作空间上对数百个机器人广泛评估我们的算法。实验结果表明，该算法比两种最先进的基线算法实现了显著的加速。

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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 : 2025-10-01 Epub 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

RelBERT: Embedding relations with language models 用语言模型嵌入关系

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence

Pub Date : 2025-10-01 Epub Date: 2025-05-15 DOI: 10.1016/j.artint.2025.104359

Asahi Ushio, Jose Camacho-Collados, Steven Schockaert

Many applications need access to background knowledge about how different concepts and entities are related. Although Large Language Models (LLM) can address this need to some extent, LLMs are inefficient and difficult to control. As an alternative, we propose to extract relation embeddings from relatively small language models. In particular, we show that masked language models such as RoBERTa can be straightforwardly fine-tuned for this purpose, using only a small amount of training data. The resulting model, which we call RelBERT, captures relational similarity in a surprisingly fine-grained way, allowing us to set a new state-of-the-art in analogy benchmarks. Crucially, RelBERT is capable of modelling relations that go well beyond what the model has seen during training. For instance, we obtained strong results on relations between named entities with a model that was only trained on lexical relations between concepts, and we observed that RelBERT can recognise morphological analogies despite not being trained on such examples. Overall, we find that RelBERT significantly outperforms strategies based on prompting language models that are several orders of magnitude larger, including recent GPT-based models and open source models.¹

许多应用程序需要访问有关不同概念和实体如何相关的背景知识。尽管大型语言模型（LLM）可以在一定程度上解决这一需求，但LLM效率低下且难以控制。作为替代方案，我们建议从相对较小的语言模型中提取关系嵌入。特别地，我们展示了像RoBERTa这样的屏蔽语言模型可以直接为此目的进行微调，只使用少量的训练数据。由此产生的模型，我们称之为RelBERT，以一种令人惊讶的细粒度方式捕获关系相似性，使我们能够在类比基准中设置新的最先进的技术。至关重要的是，RelBERT能够对远远超出模型在训练期间所看到的关系进行建模。例如，我们使用一个只在概念之间的词汇关系上训练的模型，在命名实体之间的关系上获得了强有力的结果，我们观察到RelBERT可以识别形态类比，尽管没有在这样的例子上训练。总的来说，我们发现RelBERT显著优于基于提示语言模型的策略，这些模型要大几个数量级，包括最近的基于gpt的模型和开源模型

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引用次数: 0