Artificial Intelligence最新文献

{"title":"Explanations for query answers under existential rules","authors":"İsmail İlkan Ceylan ,&nbsp;Thomas Lukasiewicz ,&nbsp;Enrico Malizia ,&nbsp;Andrius Vaicenavičius","doi":"10.1016/j.artint.2025.104294","DOIUrl":"10.1016/j.artint.2025.104294","url":null,"abstract":"<div><div>Ontology-based data access is an extensively studied paradigm aiming at improving query answers with the use of an “ontology”. An ontology is a specification of a domain of interest, which, in this context, is described via a logical theory. As a form of logical entailment, ontology-mediated query answering is fully interpretable, which makes it possible to derive explanations for ontological query answers. This is a quite important aspect, as the fact that many recent AI systems mostly operating as black boxes has led to some serious concerns. In the literature, various works on explanations in the context of description logics (DLs) have appeared, mostly focusing on explaining concept subsumption and concept unsatisfiability in the ontologies. Some works on explaining query entailment in DLs have appeared as well, however, mainly dealing with inconsistency-tolerant semantics and, actually, <em>non</em>-entailment of the queries. Surprisingly, explaining ontological query entailment has received little attention for ontology languages based on existential rules. In fact, although DLs are popular formalisms to model ontologies, it is generally agreed that rule-based ontologies are well-suited for data-intensive applications, as they allow us to conveniently deal with higher-arity relations, which naturally occur in standard relational databases. The goal of this work is to close this gap, and study the problem of explaining query entailment in the context of existential rules ontologies in terms of minimal subsets of database facts. We provide a thorough complexity analysis for several decision problems associated with minimal explanations for various classes of existential rules, and for different complexity measures.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104294"},"PeriodicalIF":5.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394775","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":"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":"2025-04-01","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":"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":"2025-04-01","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":"Improved metric distortion via threshold approvals","authors":"Elliot Anshelevich ,&nbsp;Aris Filos-Ratsikas ,&nbsp;Christopher Jerrett ,&nbsp;Alexandros A. Voudouris","doi":"10.1016/j.artint.2025.104295","DOIUrl":"10.1016/j.artint.2025.104295","url":null,"abstract":"<div><div>We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agent, when only limited information about the preferences of the agents is given. Previous work has shown that the best possible distortion one can hope to achieve is 3 when access to the ordinal preferences of the agents is given, even when the distances between alternatives in the metric space are known. We improve upon this bound of 3 by designing deterministic mechanisms that exploit a bit of cardinal information. We show that it is possible to achieve distortion <span><math><mn>1</mn><mo>+</mo><msqrt><mrow><mn>2</mn></mrow></msqrt></math></span> by using the ordinal preferences of the agents, the distances between alternatives, and a threshold approval set per agent that contains all alternatives that are at distance from the agent within an appropriately chosen factor of the minimum distance of the agents from any alternative. We show that this bound is the best possible for any deterministic mechanism in general metric spaces, and also provide improved bounds for the fundamental case of a line metric.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104295"},"PeriodicalIF":5.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072460","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":"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":"2025-04-01","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}

{"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":"2025-04-01","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":"No free lunch theorem for privacy-preserving LLM inference","authors":"Xiaojin Zhang ,&nbsp;Yahao Pang ,&nbsp;Yan Kang ,&nbsp;Wei Chen ,&nbsp;Lixin Fan ,&nbsp;Hai Jin ,&nbsp;Qiang Yang","doi":"10.1016/j.artint.2025.104293","DOIUrl":"10.1016/j.artint.2025.104293","url":null,"abstract":"<div><div>Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnish critical insights that propel the frontiers of technology and science. However, LLMs also pose privacy concerns. Users' interactions with LLMs may expose their sensitive personal or company information. A lack of robust privacy safeguards and legal frameworks could permit the unwarranted intrusion or improper handling of individual data, thereby risking infringements of privacy and the theft of personal identities. To ensure privacy, it is essential to minimize the dependency between shared prompts and private information. Various randomization approaches have been proposed to protect prompts' privacy, but they may incur utility loss compared to unprotected LLMs prompting. Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms. The current study develops a framework for inferring privacy-protected Large Language Models (LLMs) and lays down a solid theoretical basis for examining the interplay between privacy preservation and utility. The core insight is encapsulated within a theorem that is called as the NFL (abbreviation of the word No-Free-Lunch) Theorem.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104293"},"PeriodicalIF":5.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394774","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":"Beyond incompatibility: Trade-offs between mutually exclusive fairness criteria in machine learning and law","authors":"Meike Zehlike ,&nbsp;Alex Loosley ,&nbsp;Håkan Jonsson ,&nbsp;Emil Wiedemann ,&nbsp;Philipp Hacker","doi":"10.1016/j.artint.2024.104280","DOIUrl":"10.1016/j.artint.2024.104280","url":null,"abstract":"<div><div>Fair and trustworthy AI is becoming ever more important in both machine learning and legal domains. One important consequence is that decision makers must seek to guarantee a ‘fair’, i.e., non-discriminatory, algorithmic decision procedure. However, there are several competing notions of algorithmic fairness that have been shown to be mutually incompatible under realistic factual assumptions. This concerns, for example, the widely used fairness measures of ‘calibration within groups’ and ‘balance for the positive/negative class,’ which relate to accuracy, false negative and false positive rates, respectively. In this paper, we present a novel algorithm (FAir Interpolation Method: FAIM) for continuously interpolating between these three fairness criteria. Thus, an initially unfair prediction can be remedied to meet, at least partially, a desired, weighted combination of the respective fairness conditions. We demonstrate the effectiveness of our algorithm when applied to synthetic data, the COMPAS data set, and a new, real-world data set from the e-commerce sector. We provide guidance on using our algorithm in different high-stakes contexts, and we discuss to what extent FAIM can be harnessed to comply with conflicting legal obligations. The analysis suggests that it may operationalize duties in traditional legal fields, such as credit scoring and criminal justice proceedings, but also for the latest AI regulations put forth in the EU, like the Digital Markets Act and the recently enacted AI Act.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104280"},"PeriodicalIF":5.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967819","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":"Athanor: Local search over abstract constraint specifications","authors":"Saad Attieh ,&nbsp;Nguyen Dang ,&nbsp;Christopher Jefferson ,&nbsp;Ian Miguel ,&nbsp;Peter Nightingale","doi":"10.1016/j.artint.2024.104277","DOIUrl":"10.1016/j.artint.2024.104277","url":null,"abstract":"<div><div>Local search is a common method for solving combinatorial optimisation problems. We focus on general-purpose local search solvers that accept as input a constraint model — a declarative description of a problem consisting of a set of decision variables under a set of constraints. Existing approaches typically take as input models written in solver-independent constraint modelling languages like MiniZinc. The <span>Athanor</span> solver we describe herein differs in that it begins from a specification of a problem in the abstract constraint specification language <span>Essence</span>, which allows problems to be described without commitment to low-level modelling decisions through its support for a rich set of abstract types. The advantage of proceeding from <span>Essence</span> is that the structure apparent in a concise, abstract specification of a problem can be exploited to generate high quality neighbourhoods automatically, avoiding the difficult task of identifying that structure in an equivalent constraint model. Based on the twin benefits of neighbourhoods derived from high level types and the scalability derived by searching directly over those types, our empirical results demonstrate strong performance in practice relative to existing solution methods.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104277"},"PeriodicalIF":5.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925042","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":"Argumentative review aggregation and dialogical explanations","authors":"Antonio Rago ,&nbsp;Oana Cocarascu ,&nbsp;Joel Oksanen ,&nbsp;Francesca Toni","doi":"10.1016/j.artint.2025.104291","DOIUrl":"10.1016/j.artint.2025.104291","url":null,"abstract":"<div><div>The aggregation of online reviews is one of the dominant methods of quality control for users in various domains, from retail to entertainment. Consequently, <em>explainable aggregation of reviews</em> is increasingly sought-after. We introduce quantitative argumentation technology to this setting, towards automatically generating reasoned review aggregations equipped with dialogical explanations. To this end, we define a novel form of <em>argumentative dialogical agent</em> (ADA), using ontologies to harbour information from reviews into argumentation frameworks. These agents may then be evaluated with a quantitative argumentation semantics and used to mediate the generation of dialogical explanations for item recommendations based on the reviews. We show how to deploy ADAs in three different contexts in which argumentation frameworks are mined from text, guided by ontologies. First, for hotel recommendations, we use a human-authored ontology and exemplify the potential range of dialogical explanations afforded by ADAs. Second, for movie recommendations, we empirically evaluate an ADA based on a bespoke ontology (extracted semi-automatically, by natural language processing), by demonstrating that its quantitative evaluations, which are shown to satisfy desirable theoretical properties, are comparable with those on a well-known movie review aggregation website. Finally, for product recommendation in e-commerce, we use another bespoke ontology (extracted fully automatically, by natural language processing, from a website's reviews) to construct an ADA which is then empirically evaluated favourably against review aggregations from the website.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104291"},"PeriodicalIF":5.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143318247","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}