Artificial Intelligence最新文献_第4页

Optimal bailouts and strategic debt forgiveness in financial networks 金融网络中的最优救助和战略性债务减免

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

Artificial Intelligence

Pub Date : 2025-09-30 DOI: 10.1016/j.artint.2025.104424

Panagiotis Kanellopoulos , Maria Kyropoulou , Hao Zhou

A financial system is represented by a network, where nodes correspond to banks, and directed labeled edges correspond to debt contracts between banks. Once a payment schedule has been defined, the liquidity of the system is defined as the sum of total payments made in the network. Maximizing systemic liquidity is a natural objective of any financial authority, so, we study the setting where the financial authority offers bailout money to some bank(s) or forgives the debts of others in order to help them avoid costs related to default, and, hence, maximize liquidity. We investigate the approximation ratio provided by the greedy bailout policy compared to the optimal one, and we study the computational hardness of finding the optimal debt-removal and budget-constrained optimal bailout policy, respectively.

We also study financial systems from a game-theoretic standpoint. We observe that the removal of some incoming debt might be in the best interest of a bank, if that helps one of its borrowers remain solvent and avoid costs related to default. Assuming that a bank's well-being (i.e., utility) is aligned with the incoming payments they receive from the network, we define and analyze a game among banks who want to maximize their utility by strategically giving up some incoming payments. In addition, we extend the previous game by considering bailout payments. After formally defining the above games, we prove results about the existence and quality of pure Nash equilibria, as well as the computational complexity of finding such equilibria.

金融系统由网络表示，其中节点对应于银行，有方向标记的边对应于银行之间的债务合同。一旦支付计划被定义，系统的流动性就被定义为网络中所有支付的总和。最大化系统流动性是任何金融当局的自然目标，因此，我们研究了金融当局向一些银行提供救助资金或免除其他银行债务的设置，以帮助他们避免与违约相关的成本，从而最大化流动性。我们研究了贪婪救助政策与最优救助政策的近似比，并分别研究了寻找最优债务消除和预算约束的最优救助政策的计算硬度。我们也从博弈论的角度研究金融系统。我们观察到，去除一些即将到来的债务可能符合银行的最佳利益，如果这有助于其借款人之一保持偿付能力并避免与违约相关的成本。假设银行的福利（即效用）与他们从网络获得的收入一致，我们定义并分析了银行之间的博弈，这些银行希望通过战略性地放弃一些收入来最大化他们的效用。此外，我们通过考虑救助款项来延长之前的游戏。在正式定义了上述对策后，我们证明了纯纳什均衡的存在性和质量，以及寻找这种均衡的计算复杂度。

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引用次数: 0

The topology of surprise 惊喜的拓扑结构

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

Artificial Intelligence

Pub Date : 2025-09-29 DOI: 10.1016/j.artint.2025.104423

Alexandru Baltag , Nick Bezhanishvili , David Fernández-Duque

In this paper we present a topological epistemic logic, with modalities for knowledge (modelled as the universal modality), knowability (represented by the topological interior operator), and unknowability of the actual world. The last notion has a non-self-referential reading (modelled by Cantor derivative: the set of limit points of a given set) and a self-referential one (modelled by Cantor's perfect core of a given set: its largest subset without isolated points, where x is isolated iff

{x}

is open). We completely axiomatize this logic, showing that it is decidable and pspace-complete, and we apply it to the analysis of a famous epistemic puzzle: the Surprise Exam Paradox.

在本文中，我们提出了一种拓扑认知逻辑，包括知识的模态（建模为通用模态）、可知性（由拓扑内算子表示）和现实世界的不可知性。最后一个概念有一个非自指读（由康托尔导数建模：给定集合的极限点的集合）和一个自指读（由康托尔给定集合的完美核建模：它的最大的没有孤立点的子集，其中x是孤立的，如果{x}是开的）。我们完全公理化这个逻辑，表明它是可决定的和空间完备的，我们把它应用到一个著名的认知难题的分析：惊喜考试悖论。

引用次数: 0

Learngene: Inheritable “genes” in intelligent agents Learngene：智能体中可遗传的“基因”

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

Artificial Intelligence

Pub Date : 2025-09-22 DOI: 10.1016/j.artint.2025.104421

Fu Feng , Jing Wang , Xu Yang , Xin Geng

Biological intelligence has driven significant progress in artificial intelligence (AI), but a critical gap remains: biological systems inherit innate abilities from genes, with brains initialized by blueprints refined over 3.5 billion years of evolution, while machines rely heavily on inefficient, data-driven learning from scratch. This gap arises from the lack of a genetic mechanism in machines to transfer and accumulate inheritable knowledge across generations. To bridge this gap, we propose learngenes, network fragments that act as inheritable “genes” for machines. Unlike conventional knowledge transfer methods, learngenes enable efficient and universal knowledge transfer by selectively encapsulating task-agnostic knowledge. To facilitate the transfer and accumulation of task-agnostic knowledge across generations, we introduce Genetic Reinforcement Learning (GRL), a framework that simulates the learning and evolution of organisms in intelligent agents following Lamarckian principles. Through GRL, we identify learngenes as network fragments within agents' policy networks, equipping newborn agents with innate abilities for rapid adaptation to novel tasks. We demonstrate the advantages of learngene-based knowledge transfer over evolution-based search and traditional pre-trained models, and show how learngenes evolve through the accumulation of task-agnostic knowledge. Overall, this work establishes a novel paradigm for knowledge transfer and model initialization in AI, offering new possibilities for more adaptive, efficient, and scalable learning systems.

生物智能推动了人工智能（AI）的重大进步，但一个关键的差距仍然存在：生物系统从基因中继承了天生的能力，大脑是由35亿年的进化蓝图初始化的，而机器则严重依赖于低效的、数据驱动的从零开始学习。这种差距是由于机器缺乏遗传机制来传递和积累可遗传的知识。为了弥补这一差距，我们提出了学习基因，即作为机器可遗传“基因”的网络片段。与传统的知识转移方法不同，学习基因通过选择性地封装与任务无关的知识来实现高效和普遍的知识转移。为了促进任务不可知论知识在代际间的转移和积累，我们引入了遗传强化学习（GRL），这是一个遵循拉马克原理模拟智能代理中生物体的学习和进化的框架。通过GRL，我们将学习基因识别为智能体策略网络中的网络片段，为新生智能体提供快速适应新任务的先天能力。我们展示了基于学习基因的知识转移相对于基于进化的搜索和传统的预训练模型的优势，并展示了学习基因如何通过任务不可知知识的积累而进化。总的来说，这项工作为人工智能中的知识转移和模型初始化建立了一个新的范例，为更具适应性、效率和可扩展性的学习系统提供了新的可能性。

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引用次数: 0

Unsupervised sentence selection for creating a representative corpus in Turkish: An active learning approach 创建土耳其语代表性语料库的无监督句子选择：一种主动学习方法

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

Artificial Intelligence

Pub Date : 2025-09-22 DOI: 10.1016/j.artint.2025.104422

Hayri Volkan Agun

In this study, active learning methods adapted for sentence selection of Turkish sentences are evaluated through language learning with neural models. Turkish is an agglutinative language with a complex morphology, where the linguistic properties of words are encoded in suffixes. The active learning methods based on regression, clustering, language models, distance metrics, and neural networks are applied to unlabeled sentence selection. In this respect, a sentence corpus is selected from a larger corpus, with the same number of samples for each target word in intrinsic and extrinsic evaluation tasks. The selected sentences are used for the training of SkipGram, CBOW, and self-attention LSTM language models and extracted embeddings are evaluated by the semantic analogy, POS and sentiment analysis tasks. The evaluation scores of the models trained on the samples selected by the active learning method are compared. The results of the selected sentences based on language models indicate an improvement over random selection based on a static vocabulary. These results also show that the selection affects the quality of unsupervised word embedding extraction even if the target vocabulary is kept the same. Along with the accuracy, the time efficiency of the language models is shown to be better than other methods especially methods based on neural network models, and distance metrics.

在本研究中，通过神经模型的语言学习，评估了适用于土耳其语句子选择的主动学习方法。土耳其语是一种具有复杂形态学的黏合语言，其中单词的语言属性编码在后缀中。将基于回归、聚类、语言模型、距离度量和神经网络的主动学习方法应用于无标记句子的选择。在这方面，从一个更大的语料库中选择一个句子语料库，在内在和外在评价任务中，每个目标词的样本数量相同。选择的句子用于训练SkipGram、CBOW和自关注LSTM语言模型，提取的嵌入通过语义类比、POS和情感分析任务进行评估。比较了采用主动学习方法训练的模型的评价分数。基于语言模型的句子选择结果表明，与基于静态词汇表的随机选择相比，该方法有了改进。这些结果还表明，即使目标词汇保持不变，选择也会影响无监督词嵌入提取的质量。在提高准确率的同时，语言模型的时间效率也优于其他方法，特别是基于神经网络模型和距离度量的方法。

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引用次数: 0

Bridging theory and practice in bidirectional heuristic search with front-to-end consistent heuristics 基于前端一致性启发式的双向启发式搜索理论与实践的桥梁

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

Artificial Intelligence

Pub Date : 2025-09-19 DOI: 10.1016/j.artint.2025.104420

Lior Siag, Shahaf S. Shperberg

Recent research on bidirectional heuristic search (BiHS) has been shaped by the must-expand pairs (MEP) theory, which identifies the pairs of nodes that must be expanded to ensure solution optimality. Another line of research has focused on algorithms utilizing lower bounds derived from consistent heuristics during the search. This paper bridges these two approaches, offering a unified framework that demonstrates how both existing and novel algorithms can be derived from MEP theory. We introduce an extended set of bounds, encompassing both previously known and newly formulated ones. Using these bounds, we develop a range of algorithms, each employing different criteria for termination, node selection, and search direction. Finally, we empirically evaluate how these bounds and algorithms impact search efficiency.

双向启发式搜索（BiHS）的最新研究受到必须扩展对（MEP）理论的影响，该理论确定了必须扩展以确保解最优性的节点对。另一项研究集中在利用搜索过程中一致启发式导出的下界的算法上。本文将这两种方法连接起来，提供了一个统一的框架，展示了如何从MEP理论中推导出现有的和新的算法。我们引入一个扩展的界集，包括以前已知的和新制定的。利用这些边界，我们开发了一系列算法，每个算法都采用不同的终止、节点选择和搜索方向标准。最后，我们实证地评估了这些边界和算法如何影响搜索效率。

引用次数: 0

Minimax off-policy evaluation and learning with subgaussian and differentiable importance weighting 基于亚高斯和可微重要性加权的极大极小非策略评价与学习

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

Artificial Intelligence

Pub Date : 2025-09-15 DOI: 10.1016/j.artint.2025.104419

Alberto Maria Metelli, Alessio Russo, Marcello Restelli

In this work, we study the statistical properties of the off-policy estimation problem, i.e., estimating expectations under a target policy using samples collected from a different policy. We begin by presenting a novel minimax concentration lower bound that highlights the fundamental limits of off-policy estimation. We then analyze two well-known importance weighting (IW) techniques: vanilla IW and self-normalized importance weighting (SN). For both methods, we derive concentration and anti-concentration results, showing that their concentration rates are provably suboptimal compared to our lower bound. Observing that this undesired behavior arises from the heavy-tailed nature of the IW and SN estimators, we propose a new class of parametric estimators based on a transformation using the power mean (PM), which is no longer heavy-tailed. We study the theoretical properties of the PM estimator in terms of bias and variance. We show that, with suitable (possibly data-driven) tuning of its parameters, the PM estimator satisfies two key properties under certain conditions: (i) it achieves a subgaussian concentration rate that matches our lower bound and (ii) it maintains differentiability with respect to the target policy. Finally, we validate our approach through numerical simulations on both synthetic datasets and contextual bandits, comparing it against standard off-policy evaluation and learning baselines.¹

在这项工作中，我们研究了非策略估计问题的统计性质，即使用从不同策略收集的样本估计目标策略下的期望。我们首先提出了一个新的极大极小浓度下界，突出了非政策估计的基本限制。然后，我们分析了两种众所周知的重要性加权（IW）技术：香草重要性加权和自标准化重要性加权（SN）。对于这两种方法，我们都得到了浓缩和反浓缩的结果，表明与我们的下界相比，它们的浓缩率可证明是次优的。观察到这种不希望的行为是由IW和SN估计器的重尾性质引起的，我们提出了一类新的基于使用功率均值（PM）变换的参数估计器，它不再是重尾。我们从偏置和方差的角度研究了PM估计量的理论性质。我们表明，通过适当的（可能是数据驱动的）参数调整，PM估计器在某些条件下满足两个关键性质：(i)它实现了与我们的下界匹配的亚高斯浓度率；（ii）它保持了相对于目标策略的可微性。最后，我们通过在合成数据集和上下文强盗上的数值模拟来验证我们的方法，并将其与标准的非政策评估和学习基线进行比较

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引用次数: 0

On the disjunctive rational closure of a conditional knowledge base 论条件知识库的析取理性闭包

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

Artificial Intelligence

Pub Date : 2025-09-12 DOI: 10.1016/j.artint.2025.104418

Richard Booth , Ivan Varzinczak

One of the most widely investigated decision problems in symbolic AI is that of which conditional sentences of the form “if α, then normally β” should follow from a knowledge base containing this type of statements. Probably, the most notable approach to this problem is the rational closure construction put forward by Lehmann and Magidor in the'90s, which has been adapted to logical languages of various expressive powers since then. At the core of rational closure is the Rational Monotonicity property, which allows one to retain existing (defeasible) conclusions whenever new information cannot be negated by existing conclusions. As it turns out, Rational Monotonicity is not universally accepted, with many researchers advocating the investigation of weaker versions thereof leading to a larger class of consequence relations. A case in point is that of the Disjunctive Rationality property, which states that if one may draw a (defeasible) conclusion from a disjunction of premises, then one should be able to draw this conclusion from at least one of the premises taken alone. While there are convincing arguments that the rational closure forms the ‘simplest’ rational consequence relation extending a given set of conditionals, the question of what the simplest disjunctive consequence relation in this setting is has not been explored in depth. In this article, we do precisely that by motivating and proposing a concrete construction of the disjunctive rational closure of a conditional knowledge base, of which the properties and consequences of its adoption we also investigate in detail. (Previous versions of this work have been selected for presentation at the 18th International Workshop on Nonmonotonic Reasoning (NMR 2020) [1] and at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021) [2]. The present submission extends and elaborates on both papers.)

符号人工智能中最广泛研究的决策问题之一是“如果α，则通常β”形式的条件句应该从包含此类语句的知识库中跟随。对于这个问题，最值得注意的方法可能是莱曼和马吉多尔在90年代提出的理性闭包结构，从那时起，它就被适应于各种表达能力的逻辑语言。有理闭包的核心是有理单调性属性，它允许在现有结论不能否定新信息时保留现有的（可废止的）结论。事实证明，理性单调性并没有被普遍接受，许多研究人员提倡对其较弱版本的研究，从而导致更大的结果关系类别。一个恰当的例子是析取理性属性，它指出，如果一个人可以从前提的析取中得出（可推翻的）结论，那么他应该能够从至少一个单独的前提中得出这个结论。虽然有令人信服的论点认为，有理闭包形成了扩展给定条件集的“最简单”的理性推论关系，但在这种情况下，最简单的析取推论关系是什么这个问题还没有深入探讨。在本文中，我们正是通过激励和提出条件知识库的析取理性闭包的具体结构来做到这一点，我们还详细研究了其采用的性质和后果。(这项工作的先前版本已被选中在第18届非单调推理国际研讨会（NMR 2020）[1]和第35届AAAI人工智能会议（AAAI 2021）[2]上发表。本报告对这两篇论文进行了扩展和阐述。)

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引用次数: 0

Rethinking visual prompt learning as masked visual token modeling 视觉提示学习作为蒙面视觉标记建模的再思考

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

Artificial Intelligence

Pub Date : 2025-09-10 DOI: 10.1016/j.artint.2025.104417

Ning Liao , Bowen Shi , Xiaopeng Zhang , Min Cao , Junchi Yan , Qi Tian

Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as the generative pre-training ones to achieve consistency, thus improving the performance stably. However, when transferring it to the vision area, current visual prompt learning methods are almost designed on discriminative pre-trained models, and there is also a lack of careful design to unify the forms of pre-training and downstream tasks. To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification task into the pre-trained masked visual token prediction task. In addition, we develop the prototypical verbalizer for mapping the predicted visual token with implicit semantics to explicit downstream labels. To our best knowledge, VPTM is the first visual prompt method on the generative pre-trained visual model, which achieves consistency between pre-training and downstream visual classification by task reformulation. Experiments show that VPTM outperforms other visual prompt methods and achieves excellent efficiency. Moreover, the task consistency of VPTM contributes to the robustness against prompt location, prompt length and prototype dimension, and could be deployed uniformly.

在自然语言处理（NLP）中，提示学习在有效利用大规模预训练模型方面取得了巨大成功。它将下游任务重新表述为生成式预训练任务，以达到一致性，从而稳定地提高性能。然而，当将其转移到视觉区域时，目前的视觉提示学习方法几乎都是在判别性预训练模型上设计的，也缺乏将预训练和下游任务的形式统一起来的精心设计。为了探索生成式预训练视觉模型上的提示学习，并保持任务一致性，我们提出了视觉提示学习作为屏蔽视觉标记建模（VPTM），将下游的视觉分类任务转化为预训练的屏蔽视觉标记预测任务。此外，我们还开发了原型语言表达器，用于将具有隐式语义的预测视觉标记映射到显式下游标签。据我们所知，VPTM是第一个基于生成式预训练视觉模型的视觉提示方法，它通过任务重构实现了预训练与下游视觉分类的一致性。实验表明，VPTM优于其他视觉提示方法，具有优异的效率。此外，VPTM的任务一致性有助于增强对提示位置、提示长度和原型尺寸的鲁棒性，并且可以统一部署。

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引用次数: 0

Centralized training with hybrid execution in multi-agent reinforcement learning via predictive observation imputation 基于预测观测插值的多智能体强化学习集中训练混合执行

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

Artificial Intelligence

Pub Date : 2025-09-10 DOI: 10.1016/j.artint.2025.104404

Pedro P. Santos , Diogo S. Carvalho , Miguel Vasco , Alberto Sardinha , Pedro A. Santos , Ana Paiva , Francisco S. Melo

We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.

我们研究了多智能体强化学习（MARL）中的混合执行，这是一种智能体旨在利用智能体之间的信息共享来完成在执行时具有任意通信级别的合作任务的范式。在混合执行下，通信级别可以从代理之间不允许通信的设置（完全分散）到具有完全通信的设置（完全集中），但代理事先不知道它们在执行时将遇到哪个通信级别。我们贡献了MARO，一种利用自回归预测模型的方法，以集中的方式训练，来估计缺失代理在执行时的观察值。我们在标准情景和先前基准的扩展中评估MARO，以强调MARL中部分可观测性的影响。实验结果表明，我们的方法始终优于相关基线，允许代理在成功利用共享信息的同时进行错误通信。

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引用次数: 0

Planning for temporally extended goals in pure-past linear temporal logic 在纯过去线性时间逻辑中规划时间扩展目标

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

Artificial Intelligence

Pub Date : 2025-09-08 DOI: 10.1016/j.artint.2025.104409

Luigi Bonassi , Giuseppe De Giacomo , Marco Favorito , Francesco Fuggitti , Alfonso Emilio Gerevini , Enrico Scala

We study planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (ppltl) in the context of deterministic (i.e., classical) and fully observable nondeterministic (FOND) domains. ppltl is the variant of Linear-time Temporal Logic on finite traces (ltl_f) that refers to the past rather than the future. Although ppltl is as expressive as ltl_f, we show that it is computationally much more effective for planning. In particular, we show that checking the validity of a plan for a ppltl formula is Markovian. This is achieved by introducing a linear number of additional propositional variables that capture the validity of the entire formula in a modular fashion. The solution encoding introduces only a linear number of new fluents proportional to the size of the ppltl goal and does not require any additional spurious action. We implement our solution technique in a system called

Plan4Past

, which can be used alongside state-of-the-art classical and FOND planners. Our empirical analysis demonstrates the practical effectiveness of

Plan4Past

in both classical and FOND problems, showing that the resulting planner performs overall better than other planning approaches for ltl_f goals.

我们研究了在确定性（即经典）和完全可观察的非确定性（FOND）域的背景下，纯过去线性时间逻辑（ppltl）中表达的时间扩展目标的规划。ppltl是有限轨迹上的线性时间时间逻辑（ltlf）的变体，它指的是过去而不是未来。尽管ppltl与ltf一样具有表现力，但我们证明了它在规划方面的计算效率要高得多。特别地，我们证明了检验ppltl公式的计划有效性是马尔可夫的。这是通过引入线性数量的附加命题变量来实现的，这些变量以模块化的方式捕获整个公式的有效性。解决方案编码只引入与ppltl目标大小成比例的线性数量的新流，并且不需要任何额外的伪操作。我们在一个名为Plan4Past的系统中实现了我们的解决方案技术，该系统可以与最先进的经典规划和FOND规划一起使用。我们的实证分析证明了Plan4Past在经典问题和FOND问题中的实际有效性，表明所得到的规划器在实现终身目标方面的总体表现优于其他规划方法。

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引用次数: 0