Artificial Intelligence最新文献

{"title":"Disjoint projected enumeration for SAT and SMT without blocking clauses","authors":"Giuseppe Spallitta ,&nbsp;Roberto Sebastiani ,&nbsp;Armin Biere","doi":"10.1016/j.artint.2025.104346","DOIUrl":"10.1016/j.artint.2025.104346","url":null,"abstract":"<div><div>All-Solution Satisfiability (AllSAT) and its extension, All-Solution Satisfiability Modulo Theories (AllSMT), have become more relevant in recent years, mainly in formal verification and artificial intelligence applications. The goal of these problems is the enumeration of all satisfying assignments of a formula (for SAT and SMT problems, respectively), making them useful for test generation, model checking, and probabilistic inference. Nevertheless, traditional AllSAT algorithms face significant computational challenges due to the exponential growth of the search space and inefficiencies caused by blocking clauses, which cause memory blowups and degrade unit propagation performance in the long term. This paper presents two novel solvers: <span>TabularAllSAT</span>, a projected AllSAT solver, and <span>TabularAllSMT</span>, a projected AllSMT solver. Both solvers combine Conflict-Driven Clause Learning (CDCL) with chronological backtracking to improve efficiency while ensuring disjoint enumeration. To retrieve compact partial assignments we propose a novel aggressive implicant shrinking algorithm, compatible with chronological backtracking, to minimize the number of partial assignments, reducing overall search complexity. Furthermore, we extend the solver framework to handle projected enumeration and SMT formulas effectively and efficiently, adapting the baseline framework to integrate theory reasoning and the distinction between important and non-important variables. An extensive experimental evaluation demonstrates the superiority of our approach compared to state-of-the-art solvers, particularly in scenarios requiring projection and SMT-based reasoning.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"345 ","pages":"Article 104346"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911498","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}

{"title":"Deep optimal transport for domain adaptation on SPD manifolds","authors":"Ce Ju ,&nbsp;Cuntai Guan","doi":"10.1016/j.artint.2025.104347","DOIUrl":"10.1016/j.artint.2025.104347","url":null,"abstract":"<div><div>Recent progress in geometric deep learning has drawn increasing attention from the machine learning community toward domain adaptation on symmetric positive definite (SPD) manifolds—especially for neuroimaging data that often suffer from distribution shifts across sessions. These data, typically represented as covariance matrices of brain signals, inherently lie on SPD manifolds due to their symmetry and positive definiteness. However, conventional domain adaptation methods often overlook this geometric structure when applied directly to covariance matrices, which can result in suboptimal performance. To address this issue, we introduce a new geometric deep learning framework that combines optimal transport theory with the geometry of SPD manifolds. Our approach aligns data distributions while respecting the manifold structure, effectively reducing both marginal and conditional discrepancies. We validate our method on three cross-session brain-computer interface datasets—KU, BNCI2014001, and BNCI2015001—where it consistently outperforms baseline approaches while maintaining the intrinsic geometry of the data. We also provide quantitative results and visualizations to better illustrate the behavior of the learned embeddings.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"345 ","pages":"Article 104347"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906599","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}

{"title":"FedHM: Efficient federated learning for heterogeneous models via low-rank factorization","authors":"Dezhong Yao ,&nbsp;Wanning Pan ,&nbsp;Yuexin Shi ,&nbsp;Michael J. O'Neill ,&nbsp;Yutong Dai ,&nbsp;Yao Wan ,&nbsp;Peilin Zhao ,&nbsp;Hai Jin ,&nbsp;Lichao Sun","doi":"10.1016/j.artint.2025.104333","DOIUrl":"10.1016/j.artint.2025.104333","url":null,"abstract":"<div><div>One underlying assumption of recent <em>Federated Learning</em> (FL) paradigms is that all local models share an identical network architecture. However, this assumption is inefficient for heterogeneous systems where devices possess varying computation and communication capabilities. The presence of such heterogeneity among devices negatively impacts the scalability of FL and slows down the training process due to the existence of stragglers. To this end, this paper proposes a novel <em>federated compression framework for heterogeneous models</em>, named FedHM, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank global model. Furthermore, FedHM significantly reduces communication costs by utilizing low-rank models. Compared with state-of-the-art heterogeneous FL methods under various FL settings, FedHM is superior in the performance and robustness of models with different sizes. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"344 ","pages":"Article 104333"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881427","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}

{"title":"Effective and fast module extraction for nonempty ABoxes","authors":"Piero Andrea Bonatti ,&nbsp;Francesco Magliocca ,&nbsp;Iliana Mineva Petrova ,&nbsp;Luigi Sauro","doi":"10.1016/j.artint.2025.104345","DOIUrl":"10.1016/j.artint.2025.104345","url":null,"abstract":"<div><div>A deductive module of a knowledge base <span><math><mi>KB</mi></math></span> is a subset of <span><math><mi>KB</mi></math></span> that preserves a specified class of consequences. Module extraction is applied in ontology design, debugging, and reasoning. The locality-based module extractors of the OWL API are less effective when the knowledge base contains facts such as ABox assertions. The competing module extractor PrisM computes smaller modules at the cost of higher computation time. In this paper, we introduce and study a novel module extraction technique, called <em>conditional module extraction</em>, that can be applied to satisfiable <span><math><mrow><mi>SRIQ</mi></mrow><mo>(</mo><mi>D</mi><mo>)</mo></math></span> knowledge bases. Experimental analysis shows that conditional module extraction constitutes an appealing alternative to PrisM and to the locality-based extractors of the OWL API, when the ABox is nonempty.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"344 ","pages":"Article 104345"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881426","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}

{"title":"The incentive guarantees behind Nash welfare in divisible resources allocation","authors":"Xiaohui Bei ,&nbsp;Biaoshuai Tao ,&nbsp;Jiajun Wu ,&nbsp;Mingwei Yang","doi":"10.1016/j.artint.2025.104335","DOIUrl":"10.1016/j.artint.2025.104335","url":null,"abstract":"<div><div>We study the problem of allocating divisible resources among <em>n</em> agents, hopefully in a fair and efficient manner. With the presence of strategic agents, additional incentive guarantees are also necessary, and the problem of designing fair and efficient mechanisms becomes much less tractable. While there are flourishing positive results against strategic agents for homogeneous divisible items, very few of them are known to hold in cake cutting.</div><div>We show that the Maximum Nash Welfare (MNW) mechanism, which provides desirable fairness and efficiency guarantees and achieves an <em>incentive ratio</em> of 2 for homogeneous divisible items, also has an incentive ratio of 2 in cake cutting. Remarkably, this result holds even without the free disposal assumption, which is hard to get rid of in the design of truthful cake cutting mechanisms.</div><div>Moreover, we show that, for cake cutting, the Partial Allocation (PA) mechanism proposed by Cole et al. <span><span>[1]</span></span>, which is truthful and <span><math><mn>1</mn><mo>/</mo><mi>e</mi></math></span>-MNW for homogeneous divisible items, has an incentive ratio between <span><math><mo>[</mo><msup><mrow><mi>e</mi></mrow><mrow><mn>1</mn><mo>/</mo><mi>e</mi></mrow></msup><mo>,</mo><mi>e</mi><mo>]</mo></math></span> and when randomization is allowed, can be turned to be truthful in expectation. Given two alternatives for a trade-off between incentive ratio and Nash welfare provided by the MNW and PA mechanisms, we establish an interpolation between them for both cake cutting and homogeneous divisible items.</div><div>Finally, we study the optimal incentive ratio achievable by envy-free cake cutting mechanisms. We first give an envy-free mechanism for two agents with an incentive ratio of 4/3. Then, we show that any envy-free cake cutting mechanism with the connected pieces constraint has an incentive ratio of <span><math><mi>Θ</mi><mo>(</mo><mi>n</mi><mo>)</mo></math></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"344 ","pages":"Article 104335"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856006","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}

{"title":"Learning optimal contracts with small action spaces","authors":"Francesco Bacchiocchi,&nbsp;Matteo Castiglioni,&nbsp;Nicola Gatti,&nbsp;Alberto Marchesi","doi":"10.1016/j.artint.2025.104334","DOIUrl":"10.1016/j.artint.2025.104334","url":null,"abstract":"<div><div>We study <em>principal-agent problems</em> in which a principal commits to an outcome-dependent payment scheme—called <em>contract</em>—in order to induce an agent to take a costly, unobservable action leading to favorable outcomes. We consider a generalization of the classical (single-round) version of the problem in which the principal interacts with the agent by committing to contracts over multiple rounds. The principal has no information about the agent, and they have to learn an optimal contract by only observing the outcome realized at each round. We focus on settings in which the <em>size of the agent's action space is small</em>. We design an algorithm that learns an approximately-optimal contract with high probability in a number of rounds polynomial in the size of the outcome space, when the number of actions is constant. Our algorithm solves an open problem by Zhu et al. <span><span>[1]</span></span>. Moreover, it can also be employed to provide a <span><math><mover><mrow><mi>O</mi></mrow><mrow><mo>˜</mo></mrow></mover><mo>(</mo><msup><mrow><mi>T</mi></mrow><mrow><mn>4</mn><mo>/</mo><mn>5</mn></mrow></msup><mo>)</mo></math></span> regret bound in the related online learning setting in which the principal aims at maximizing their cumulative utility over rounds, considerably improving previously-known regret bounds.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"344 ","pages":"Article 104334"},"PeriodicalIF":5.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859966","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}

{"title":"The influence of dimensions on the complexity of computing decision trees","authors":"Stephen Kobourov ,&nbsp;Maarten Löffler ,&nbsp;Fabrizio Montecchiani ,&nbsp;Marcin Pilipczuk ,&nbsp;Ignaz Rutter ,&nbsp;Raimund Seidel ,&nbsp;Manuel Sorge ,&nbsp;Jules Wulms","doi":"10.1016/j.artint.2025.104322","DOIUrl":"10.1016/j.artint.2025.104322","url":null,"abstract":"<div><div>A decision tree recursively splits a feature space <span><math><msup><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msup></math></span> and then assigns class labels based on the resulting partition. Decision trees have been part of the basic machine-learning toolkit for decades. A large body of work considers heuristic algorithms that compute a decision tree from training data, usually aiming to minimize in particular the size of the resulting tree. In contrast, little is known about the complexity of the underlying computational problem of computing a minimum-size tree for the given training data. We study this problem with respect to the number <em>d</em> of dimensions of the feature space <span><math><msup><mrow><mi>R</mi></mrow><mrow><mi>d</mi></mrow></msup></math></span>, which contains <em>n</em> training examples. We show that it can be solved in <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn><mi>d</mi><mo>+</mo><mn>1</mn></mrow></msup><mo>)</mo></math></span> time, but under reasonable complexity-theoretic assumptions it is not possible to achieve <span><math><mi>f</mi><mo>(</mo><mi>d</mi><mo>)</mo><mo>⋅</mo><msup><mrow><mi>n</mi></mrow><mrow><mi>o</mi><mo>(</mo><mi>d</mi><mo>/</mo><mi>log</mi><mo>⁡</mo><mi>d</mi><mo>)</mo></mrow></msup></math></span> running time. The problem is solvable in <span><math><msup><mrow><mo>(</mo><mi>d</mi><mi>R</mi><mo>)</mo></mrow><mrow><mi>O</mi><mo>(</mo><mi>d</mi><mi>R</mi><mo>)</mo></mrow></msup><mo>⋅</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>1</mn><mo>+</mo><mi>o</mi><mo>(</mo><mn>1</mn><mo>)</mo></mrow></msup></math></span> time if there are exactly two classes and <em>R</em> is an upper bound on the number of tree leaves labeled with the first class.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"343 ","pages":"Article 104322"},"PeriodicalIF":5.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The value of real-time automated explanations in stochastic planning","authors":"Claudia V. Goldman ,&nbsp;Ronit Bustin ,&nbsp;Wenyuan Qi ,&nbsp;Zhengyu Xing ,&nbsp;Rachel McPhearson-White ,&nbsp;Sally Rogers","doi":"10.1016/j.artint.2025.104323","DOIUrl":"10.1016/j.artint.2025.104323","url":null,"abstract":"<div><div>Recently, we are witnessing an increase in computation power and memory, leading to strong AI algorithms becoming applicable in areas affecting our daily lives. We focus on AI planning solutions for complex, real-life decision-making problems under uncertainty, such as autonomous driving. Human trust in such AI-based systems is essential for their acceptance and market penetration. Moreover, users need to establish appropriate levels of trust to benefit the most from these systems. Previous studies have motivated this work, showing that users can benefit from receiving (handcrafted) information about the reasoning of a stochastic AI planner, for example, controlling automated driving maneuvers. Our solution to automating these hand-crafted notifications with explainable AI algorithms, XAI, includes studying: (1) what explanations can be generated from an AI planning system, applied to a real-world problem, in real-time? What is that content that can be processed from a planner's reasoning that can help users understand and trust the system controlling a behavior they are experiencing? (2) when can this information be displayed? and (3) how shall we display this information to an end user? The value of these computed XAI notifications has been assessed through an online user study with 800 participants, experiencing simulated automated driving scenarios. Our results show that real time XAI notifications decrease significantly subjective misunderstanding of participants compared to those that received only a dynamic HMI display. Also, our XAI solution significantly increases the level of understanding of participants with prior ADAS experience and of participants that lack such experience but have non-negative prior trust to ADAS features. The level of trust significantly increases when XAI was provided to a more restricted set of the participants, including those over 60 years old, with prior ADAS experience and non-negative prior trust attitude to automated features.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"343 ","pages":"Article 104323"},"PeriodicalIF":5.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815962","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}

{"title":"Drawing a map of elections","authors":"Stanisław Szufa ,&nbsp;Niclas Boehmer ,&nbsp;Robert Bredereck ,&nbsp;Piotr Faliszewski ,&nbsp;Rolf Niedermeier ,&nbsp;Piotr Skowron ,&nbsp;Arkadii Slinko ,&nbsp;Nimrod Talmon","doi":"10.1016/j.artint.2025.104332","DOIUrl":"10.1016/j.artint.2025.104332","url":null,"abstract":"<div><div>Our main contribution is the introduction of the map of elections framework. A map of elections consists of three main elements: (1) a dataset of elections (i.e., collections of ordinal votes over given sets of candidates), (2) a way of measuring similarities between these elections, and (3) a representation of the elections in the 2D Euclidean space as points, so that the more similar two elections are, the closer are their points. In our maps, we mostly focus on datasets of synthetic elections, but we also show an example of a map over real-life ones. To measure similarities, we would have preferred to use, e.g., the isomorphic swap distance, but this is infeasible due to its high computational complexity. Hence, we propose polynomial-time computable positionwise distance and use it instead. Regarding the representations in 2D Euclidean space, we mostly use the Kamada-Kawai algorithm, but we also show two alternatives. We develop the necessary theoretical results to form our maps and argue experimentally that they are accurate and credible. Further, we show how coloring the elections in a map according to various criteria helps in analyzing results of a number of experiments. In particular, we show colorings according to the scores of winning candidates or committees, running times of ILP-based winner determination algorithms, and approximation ratios achieved by particular algorithms.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"343 ","pages":"Article 104332"},"PeriodicalIF":5.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"(Re)Conceptualizing trustworthy AI: A foundation for change","authors":"Christopher D. Wirz ,&nbsp;Julie L. Demuth ,&nbsp;Ann Bostrom ,&nbsp;Mariana G. Cains ,&nbsp;Imme Ebert-Uphoff ,&nbsp;David John Gagne II ,&nbsp;Andrea Schumacher ,&nbsp;Amy McGovern ,&nbsp;Deianna Madlambayan","doi":"10.1016/j.artint.2025.104309","DOIUrl":"10.1016/j.artint.2025.104309","url":null,"abstract":"<div><div>Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"342 ","pages":"Article 104309"},"PeriodicalIF":5.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}