Artificial Intelligence最新文献

{"title":"The topology of surprise","authors":"Alexandru Baltag ,&nbsp;Nick Bezhanishvili ,&nbsp;David Fernández-Duque","doi":"10.1016/j.artint.2025.104423","DOIUrl":"10.1016/j.artint.2025.104423","url":null,"abstract":"<div><div>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 <em>x</em> is isolated iff <span><math><mo>{</mo><mi>x</mi><mo>}</mo></math></span> is open). We completely axiomatize this logic, showing that it is decidable and <span>pspace</span>-complete, and we apply it to the analysis of a famous epistemic puzzle: the Surprise Exam Paradox.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"349 ","pages":"Article 104423"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145189732","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":"Pandora's box problem with time constraints","authors":"Georgios Amanatidis ,&nbsp;Ben Berger ,&nbsp;Tomer Ezra ,&nbsp;Michal Feldman ,&nbsp;Federico Fusco ,&nbsp;Rebecca Reiffenhäuser ,&nbsp;Artem Tsikiridis","doi":"10.1016/j.artint.2025.104426","DOIUrl":"10.1016/j.artint.2025.104426","url":null,"abstract":"<div><div>The Pandora's Box problem models the search for the best alternative when evaluation is costly. In the simplest variant, a decision maker is presented with <em>n</em> boxes, each associated with a cost of inspection and a hidden random reward. The decision maker inspects a subset of these boxes one after the other, in a possibly adaptive order, and gains the difference between the largest revealed reward and the sum of the inspection costs. Although this classic version is well understood (Weitzman 1979), there is a flourishing recent literature on variants of the problem. Here we introduce a general framework—the Pandora's Box Over Time problem—that captures a wide range of variants where time plays a role, e.g., by constraining the schedules of exploration and influencing costs and rewards. In our framework, boxes have time-dependent rewards and costs, whereas inspection may require a box-specific processing time. Moreover, once a box is inspected, its reward may deteriorate over time. Our main result is an efficient constant-factor approximation to the optimal strategy for the Pandora's Box Over Time problem, which is generally NP-hard to compute. We further obtain improved results for the natural special cases where boxes have no processing time, boxes are available only in specific time slots, or when costs and reward distributions are time-independent (but rewards may still deteriorate after inspection).</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"349 ","pages":"Article 104426"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263923","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":"Constraints and lifting-based (conditional) preferences in abstract argumentation","authors":"Gianvincenzo Alfano,&nbsp;Sergio Greco,&nbsp;Francesco Parisi,&nbsp;Irina Trubitsyna","doi":"10.1016/j.artint.2025.104437","DOIUrl":"10.1016/j.artint.2025.104437","url":null,"abstract":"<div><div>Dealing with controversial information is an important issue in several application contexts. Formal argumentation enables reasoning on arguments for and against a claim to decide on an outcome. Abstract Argumentation Framework (AF) has emerged as a central formalism in argument-based reasoning. In recent years there has been an increasing interest in extending AF to facilitate the knowledge representation and reasoning process. In this paper, we present an extension of AF that allows for the representation of labelled constraints and labelled preferences. A labelled argument is of the form <span><math><mrow><mi>in</mi></mrow><mo>(</mo><mi>a</mi><mo>)</mo></math></span>, <span><math><mrow><mi>out</mi></mrow><mo>(</mo><mi>a</mi><mo>)</mo></math></span>, or <span><math><mrow><mi>und</mi></mrow><mo>(</mo><mi>a</mi><mo>)</mo></math></span>, where <em>a</em> is an argument, whereas <strong>in</strong>, <strong>out</strong>, and <strong>und</strong> denote the acceptance status (i.e., accepted, rejected, undecided, respectively) of the specified argument. We start by considering an extension of AF with labelled constraints, namely <em>Labelled Constrained AF</em> (LCAF), then we focus on AF with labelled preferences (<em>Labelled Preference-based AF</em>, LPAF for short) and, finally, we introduce a general framework called <em>Labelled Preference-based Constrained AF</em> (LPCAF) that combines AF, labelled constraints, and labelled preferences. We also investigate an extension of AF with labelled conditional (or extended) preferences, namely <em>Labelled extended Preference-based AF</em> (LePAF), and its further combination with labelled constraints (<em>Labelled extended Preference-based Constrained AF</em>, LePCAF for short). Herein, conditional preferences are of the form <span><math><mi>a</mi><mo>&gt;</mo><mi>b</mi><mo>←</mo></math></span> <em>body</em>, where <strong>a</strong> and <strong>b</strong> are labelled arguments, whereas <em>body</em> is a propositional formula over labelled arguments. For each framework, we define its syntax and semantics, and investigate the computational complexity of four canonical argumentation problems: <em>existence</em>, <em>verification</em>, and <em>credulous</em> and <em>skeptical acceptance</em>, under the well-known <em>complete</em>, <em>stable</em>, <em>semi-stable</em>, and <em>preferred</em> semantics.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"349 ","pages":"Article 104437"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322151","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":"Optimal bailouts and strategic debt forgiveness in financial networks","authors":"Panagiotis Kanellopoulos ,&nbsp;Maria Kyropoulou ,&nbsp;Hao Zhou","doi":"10.1016/j.artint.2025.104424","DOIUrl":"10.1016/j.artint.2025.104424","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"349 ","pages":"Article 104424"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263920","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":"Arc-consistency with linear programming reduced costs (applied to stable set in chordal graphs)","authors":"Guillaume Claus ,&nbsp;Hadrien Cambazard ,&nbsp;Hugo Apeloig ,&nbsp;Pierre Hoppenot","doi":"10.1016/j.artint.2025.104438","DOIUrl":"10.1016/j.artint.2025.104438","url":null,"abstract":"<div><div>A well known technique to reduce the search space in integer programming is known as <em>variable fixing</em> or <em>reduced cost strengthening</em>. The reduced costs given by an optimal dual solution of the linear relaxation can be used to strengthen the bounds of the variables but this filtering is incomplete. We show how reduced costs can be used to achieve Arc-Consistency (AC), <em>i.e.</em> a complete filtering, of a global constraint with a cost variable and an assignment cost for each value. We assume that an ideal Integer Linear Programming (ILP) formulation is available i.e. the convex hull of the characteristic vectors of the supports is known. A detailed analysis of reduced cost based filtering is proposed. We characterize arc-consistency based on complementary slackness <em>i.e.</em> completeness of reasoning as opposed to only optimality. We also give a simple sufficient condition allowing a set of dual solutions to ensure arc-consistency through reduced costs. In practice, when the constraint has a such an ideal ILP, <em>n</em> dual solutions are always enough to achieve AC (where <em>n</em> is the number of variables of the global constraint). It extends the work presented in <span><span>[26]</span></span> for satisfaction problems and in <span><span>[17]</span></span> for the specific case of the minimum weighted alldifferent constraint. Our analysis is illustrated on constraints related to the assignment and shortest path problem and also demonstrated on the weighted stable set problem in chordal graphs. A novel AC algorithm is proposed in this latter case based on reduced costs.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"349 ","pages":"Article 104438"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145359752","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":"Incentives for responsiveness, instrumental control and impact","authors":"Ryan Carey ,&nbsp;Eric Langlois ,&nbsp;Chris van Merwijk ,&nbsp;Shane Legg ,&nbsp;Tom Everitt","doi":"10.1016/j.artint.2025.104408","DOIUrl":"10.1016/j.artint.2025.104408","url":null,"abstract":"<div><div>We introduce three concepts that describe an agent's incentives: response incentives indicate which variables in the environment, such as sensitive demographic information, affect the decision under the optimal policy. Instrumental control incentives indicate whether an agent's policy is chosen to manipulate part of its environment, such as the preferences or instructions of a user. Impact incentives indicate which variables an agent will affect, intentionally or otherwise. For each concept, we establish sound and complete graphical criteria, and discuss general classes of techniques that may be used to produce incentives for safe and fair agent behaviour. Finally, we outline how these notions may be generalised to multi-decision settings.</div><div>This journal paper extends our conference publication “Agent Incentives: A Causal Perspective”: the material on response incentives and instrumental control incentives is updated, while the work on impact incentives and multi-decision settings is entirely new.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104408"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018406","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":"Algebras of actions in an agent's representations of the world","authors":"Alexander Dean,&nbsp;Eduardo Alonso,&nbsp;Esther Mondragón","doi":"10.1016/j.artint.2025.104403","DOIUrl":"10.1016/j.artint.2025.104403","url":null,"abstract":"<div><div>Learning efficient representations allows robust processing of data, data that can then be generalised across different tasks and domains, and it is thus paramount in various areas of Artificial Intelligence, including computer vision, natural language processing and reinforcement learning, among others. Within the context of reinforcement learning, we propose in this paper a mathematical framework to learn representations by extracting the algebra of the transformations of worlds from the perspective of an agent. As a starting point, we use our framework to reproduce representations from the symmetry-based disentangled representation learning (SBDRL) formalism proposed by <span><span>[1]</span></span> and prove that, although useful, they are restricted to transformations that respond to the properties of algebraic groups. We then generalise two important results of SBDRL –the equivariance condition and the disentangling definition– from only working with group-based symmetry representations to working with representations capturing the transformation properties of worlds for any algebra, using examples common in reinforcement learning and generated by an algorithm that computes their corresponding Cayley tables. Finally, we combine our generalised equivariance condition and our generalised disentangling definition to show that disentangled sub-algebras can each have their own individual equivariance conditions, which can be treated independently, using category theory. In so doing, our framework offers a rich formal tool to represent different types of symmetry transformations in reinforcement learning, extending the scope of previous proposals and providing Artificial Intelligence developers with a sound foundation to implement efficient applications.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104403"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886771","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":"Minimax off-policy evaluation and learning with subgaussian and differentiable importance weighting","authors":"Alberto Maria Metelli,&nbsp;Alessio Russo,&nbsp;Marcello Restelli","doi":"10.1016/j.artint.2025.104419","DOIUrl":"10.1016/j.artint.2025.104419","url":null,"abstract":"<div><div>In this work, we study the statistical properties of the <em>off-policy estimation</em> 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 <em>importance weighting</em> (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 <em>heavy-tailed</em> nature of the IW and SN estimators, we propose a new class of parametric estimators based on a transformation using the <em>power mean</em> (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: (<em>i</em>) it achieves a <em>subgaussian</em> concentration rate that matches our lower bound and (<em>ii</em>) 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.<span><span>¹</span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104419"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094875","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 design of truthful mechanisms for the capacitated facility location problem with two and more facilities","authors":"Gennaro Auricchio ,&nbsp;Zihe Wang ,&nbsp;Jie Zhang","doi":"10.1016/j.artint.2025.104390","DOIUrl":"10.1016/j.artint.2025.104390","url":null,"abstract":"<div><div>In this paper, we explore the Mechanism Design aspects of the <em>m</em>-Capacitated Facility Location Problem (<em>m</em>-CFLP) on a line, focusing on two frameworks. In the first framework, the number of facilities is arbitrary, all facilities share the same capacity, and the number of agents matches the total capacity of the facilities. In the second framework, we need to locate two facilities, each with a capacity equal to at least half the number of agents. For both frameworks, we propose truthful mechanisms with bounded approximation ratios in terms of Social Cost (SC) and Maximum Cost (MC). When <span><math><mi>m</mi><mo>&gt;</mo><mn>2</mn></math></span>, our results stand in contrast to the impossibility results known for the classical <em>m</em>-Facility Location Problem, where capacity constraints are absent. Moreover, all the proposed mechanisms are optimal with respect to MC and either optimal or near-optimal with respect to the SC among anonymous mechanisms. We then establish lower bounds on the approximation ratios that any truthful and deterministic mechanism achieves with respect to SC and MC for both frameworks. Lastly, we run several numerical experiments to empirically evaluate the performances of our mechanisms with respect to the SC or the MC. Our empirical analysis shows that our proposed mechanisms outperform all previously proposed mechanisms applicable in this setting.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104390"},"PeriodicalIF":5.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581145","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":"Interpreting capsule networks for image classification by routing path visualization","authors":"Amanjot Bhullar ,&nbsp;Michael Czomko ,&nbsp;R. Ayesha Ali ,&nbsp;Douglas L. Welch","doi":"10.1016/j.artint.2025.104395","DOIUrl":"10.1016/j.artint.2025.104395","url":null,"abstract":"<div><div>Artificial neural networks are popular for computer vision as they often give state-of-the-art performance, but are difficult to interpret because of their complexity. This black box modeling is especially troubling when the application concerns human well-being such as in medical image analysis or autonomous driving. In this work, we propose a technique called routing path visualization for capsule networks, which reveals how much of each region in an image is routed to each capsule. In turn, this technique can be used to interpret the entity that a given capsule detects, and speculate how the network makes a prediction. We demonstrate our new visualization technique on several real world datasets. Experimental results suggest that routing path visualization can precisely localize the predicted class from an image, even though the capsule networks are trained using just images and their respective class labels, without additional information defining the location of the class in the image.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104395"},"PeriodicalIF":5.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144665065","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}