Artificial Intelligence最新文献

{"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":"2025-03-28","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":"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":"2025-03-07","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":"ICCMA 2023: 5th International Competition on Computational Models of Argumentation","authors":"Matti Järvisalo ,&nbsp;Tuomo Lehtonen ,&nbsp;Andreas Niskanen","doi":"10.1016/j.artint.2025.104311","DOIUrl":"10.1016/j.artint.2025.104311","url":null,"abstract":"<div><div>The study of computational models of argumentation and the development of practical automated approaches to reasoning over the models has developed into a vibrant area of artificial intelligence research in recent years. The series of International Competitions on Computational Models of Argumentation (ICCMA) aims at nurturing research and development of practical reasoning algorithms for models of argumentation. Organized biennially, the ICCMA competitions provide a snapshot of the current state of the art in algorithm implementations for central fundamental reasoning tasks over models of argumentation. The year 2023 marked the 5th instantiation of International Competitions on Computational Models of Argumentation, ICCMA 2023. We provide a comprehensive overview of ICCMA 2023, including details on the various new developments introduced in 2023, overview of the participating solvers, extensive details on the competition benchmarks and results, as well as lessons learned.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"342 ","pages":"Article 104311"},"PeriodicalIF":5.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582668","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":"Lifted inference beyond first-order logic","authors":"Sagar Malhotra ,&nbsp;Davide Bizzaro ,&nbsp;Luciano Serafini","doi":"10.1016/j.artint.2025.104310","DOIUrl":"10.1016/j.artint.2025.104310","url":null,"abstract":"<div><div>Weighted First Order Model Counting (WFOMC) is fundamental to probabilistic inference in statistical relational learning models. As WFOMC is known to be intractable in general (#P-complete), logical fragments that admit polynomial time WFOMC are of significant interest. Such fragments are called <em>domain liftable</em>. Recent works have shown that the two-variable fragment of first order logic extended with counting quantifiers (C²) is domain-liftable. However, many properties of real-world data, like <em>acyclicity</em> in citation networks and <em>connectivity</em> in social networks, cannot be modeled in C², or first order logic in general. In this work, we expand the domain liftability of C² with multiple such properties. We show that any C² sentence remains domain liftable when one of its relations is restricted to represent a directed acyclic graph, a connected graph, a tree (resp. a directed tree) or a forest (resp. a directed forest). All our results rely on a novel and general methodology of <em>counting by splitting</em>. Besides their application to probabilistic inference, our results provide a general framework for counting combinatorial structures. We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"342 ","pages":"Article 104310"},"PeriodicalIF":5.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479252","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":"2025-02-22","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}

{"title":"Stochastic population update can provably be helpful in multi-objective evolutionary algorithms","authors":"Chao Bian ,&nbsp;Yawen Zhou ,&nbsp;Miqing Li ,&nbsp;Chao Qian","doi":"10.1016/j.artint.2025.104308","DOIUrl":"10.1016/j.artint.2025.104308","url":null,"abstract":"<div><div>Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the best solutions from the current population and newly-generated solutions (irrespective of the selection criteria used such as Pareto dominance, crowdedness and indicators). In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104308"},"PeriodicalIF":5.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430306","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":"Grounded predictions of teamwork as a one-shot game: A multiagent multi-armed bandits approach","authors":"Alejandra López de Aberasturi Gómez,&nbsp;Carles Sierra,&nbsp;Jordi Sabater-Mir","doi":"10.1016/j.artint.2025.104307","DOIUrl":"10.1016/j.artint.2025.104307","url":null,"abstract":"<div><div>Humans possess innate collaborative capacities. However, effective teamwork often remains challenging. This study delves into the feasibility of collaboration within teams of rational, self-interested agents who engage in teamwork without the obligation to contribute. Drawing from psychological and game theoretical frameworks, we formalise teamwork as a one-shot aggregative game, integrating insights from Steiner's theory of group productivity. We characterise this novel game's Nash equilibria and propose a multiagent multi-armed bandit system that learns to converge to approximations of such equilibria. Our research contributes value to the areas of game theory and multiagent systems, paving the way for a better understanding of voluntary collaborative dynamics. We examine how team heterogeneity, task typology, and assessment difficulty influence agents' strategies and resulting teamwork outcomes. Finally, we empirically study the behaviour of work teams under incentive systems that defy analytical treatment. Our agents demonstrate human-like behaviour patterns, corroborating findings from social psychology research.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104307"},"PeriodicalIF":5.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422377","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":"Grammar induction from visual, speech and text","authors":"Yu Zhao ,&nbsp;Hao Fei ,&nbsp;Shengqiong Wu ,&nbsp;Meishan Zhang ,&nbsp;Min Zhang ,&nbsp;Tat-seng Chua","doi":"10.1016/j.artint.2025.104306","DOIUrl":"10.1016/j.artint.2025.104306","url":null,"abstract":"<div><div>Grammar Induction (GI) seeks to uncover the underlying grammatical rules and linguistic patterns of a language, positioning it as a pivotal research topic within Artificial Intelligence (AI). Although extensive research in GI has predominantly focused on text or other singular modalities, we reveal that GI could significantly benefit from rich heterogeneous signals, such as text, vision, and acoustics. In the process, features from distinct modalities essentially serve complementary roles to each other. With such intuition, this work introduces a novel <em>unsupervised visual-audio-text grammar induction</em> task (named <strong>VAT-GI</strong>), to induce the constituent grammar trees from parallel images, text, and speech inputs. Inspired by the fact that language grammar natively exists beyond the texts, we argue that the text has not to be the predominant modality in grammar induction. Thus we further introduce a <em>textless</em> setting of VAT-GI, wherein the task solely relies on visual and auditory inputs. To approach the task, we propose a visual-audio-text inside-outside recursive autoencoder (<strong>VaTiora</strong>) framework, which leverages rich modal-specific and complementary features for effective grammar parsing. Besides, a more challenging benchmark data is constructed to assess the generalization ability of VAT-GI system. Experiments on two benchmark datasets demonstrate that our proposed VaTiora system is more effective in incorporating the various multimodal signals, and also presents new state-of-the-art performance of VAT-GI. Further in-depth analyses are shown to gain a deep understanding of the VAT-GI task and how our VaTiora system advances. Our code and data: <span><span>https://github.com/LLLogen/VAT-GI/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104306"},"PeriodicalIF":5.1,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143418533","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":"On the computation of mixed strategies for security games with general defending requirements","authors":"Rufan Bai ,&nbsp;Haoxing Lin ,&nbsp;Xiaowei Wu ,&nbsp;Minming Li ,&nbsp;Weijia Jia","doi":"10.1016/j.artint.2025.104297","DOIUrl":"10.1016/j.artint.2025.104297","url":null,"abstract":"<div><div>The Stackelberg security game is played between a defender and an attacker, where the defender needs to allocate a limited amount of resources to multiple targets in order to minimize the loss due to adversarial attacks by the attacker. While allowing targets to have different values, classic settings often assume uniform requirements for defending the targets. This enables existing results that study mixed strategies (randomized allocation algorithms) to adopt a <em>compact representation</em> of the mixed strategies.</div><div>In this work, we initiate the study of mixed strategies for security games in which the targets can have different defending requirements. In contrast to the case of uniform defending requirements, for which an optimal mixed strategy can be computed efficiently, we show that computing the optimal mixed strategy is <span>NP</span>-hard for the general defending requirements setting. However, we show strong upper and lower bounds for the optimal mixed strategy defending result. Additionally, we extend our analysis to study uniform attack settings on these security games.</div><div>We propose an efficient close-to-optimal <span>Patching</span> algorithm that computes mixed strategies using only a few pure strategies. Furthermore, we study the setting when the game is played on a network and resource sharing is enabled between neighboring targets. We show the effectiveness of our algorithm in various large real-world datasets, addressing both uniform and general defending requirements.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104297"},"PeriodicalIF":5.1,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388406","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":"IID prophet inequality with a single data point","authors":"Yilong Feng ,&nbsp;Bo Li ,&nbsp;Haolong Li ,&nbsp;Xiaowei Wu ,&nbsp;Yutong Wu","doi":"10.1016/j.artint.2025.104296","DOIUrl":"10.1016/j.artint.2025.104296","url":null,"abstract":"<div><div>In this work, we study the single-choice prophet inequality problem, where a seller encounters a sequence of <em>n</em> online bids. These bids are modeled as independent and identically distributed (i.i.d.) random variables drawn from an unknown distribution. Upon the revelation of each bid's value, the seller must make an immediate and irrevocable decision on whether to accept the bid and sell the item to the bidder. The objective is to maximize the competitive ratio between the expected gain of the seller and that of the maximum bid. It is shown by Correa et al. <span><span>[1]</span></span> that when the distribution is unknown or only <span><math><mi>o</mi><mo>(</mo><mi>n</mi><mo>)</mo></math></span> uniform samples from the distribution are given, the best an algorithm can do is <span><math><mn>1</mn><mo>/</mo><mi>e</mi></math></span>-competitive. In contrast, when the distribution is known <span><span>[2]</span></span>, or when <span><math><mi>Ω</mi><mo>(</mo><mi>n</mi><mo>)</mo></math></span> uniform samples are given <span><span>[3]</span></span>, the optimal competitive ratio of 0.7451 can be achieved. In this paper, we study the setting when the seller has access to a single point in the cumulative density function of the distribution, which can be learned from historical sales data. We investigate how effectively this data point can be used to design competitive algorithms. Motivated by the algorithm for the secretary problem, we propose the observe-and-accept algorithm that sets a threshold in the first phase using the data point and adopts the highest bid from the first phase as the threshold for the second phase. It can be viewed as a natural combination of the single-threshold algorithm for prophet inequality and the secretary problem algorithm. We show that our algorithm achieves a good competitive ratio for a wide range of data points, reaching up to 0.6785-competitive as <span><math><mi>n</mi><mo>→</mo><mo>∞</mo></math></span> for certain data points. Additionally, we study an extension of the algorithm that utilizes more than two phases and show that the competitive ratio can be further improved to at least 0.6862.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104296"},"PeriodicalIF":5.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388405","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}