Artificial Intelligence最新文献

{"title":"Factored-reward bandits with intermediate observations: Regret minimization and best arm identification","authors":"Marco Mussi ,&nbsp;Simone Drago ,&nbsp;Marcello Restelli,&nbsp;Alberto Maria Metelli","doi":"10.1016/j.artint.2025.104362","DOIUrl":"10.1016/j.artint.2025.104362","url":null,"abstract":"<div><div>In several real-world sequential decision problems, at every step, the learner is required to select different actions. Every action affects a specific part of the system and generates an observable intermediate effect. In this paper, we introduce the Factored-Reward Bandits (FRBs), a novel setting able to effectively capture and exploit the structure of this class of scenarios, where the reward is computed as the product of the action intermediate observations. We characterize the statistical complexity of the learning problem in the FRBs, by deriving worst-case and asymptotic instance-dependent regret lower bounds. Then, we devise and analyze two regret minimization algorithms. The former, <span>F-UCB</span>, is an anytime optimistic approach matching the worst-case lower bound (up to logarithmic factors) but fails to perform optimally from the instance-dependent perspective. The latter, <span>F-Track</span>, is a bound-tracking approach, that enjoys optimal asymptotic instance-dependent regret guarantees. Finally, we study the problem of performing best arm identification in this setting. We derive an error probability lower bound, and we develop <span>F-SR</span>, a nearly optimal rejection-based algorithm for identifying the best action vector, given a time budget.<span><span>²</span></span></div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104362"},"PeriodicalIF":5.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263063","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":"Introduction to open-world AI","authors":"Lawrence Holder ,&nbsp;Pat Langley ,&nbsp;Bryan Loyall ,&nbsp;Ted Senator","doi":"10.1016/j.artint.2025.104393","DOIUrl":"10.1016/j.artint.2025.104393","url":null,"abstract":"<div><div>Open-world AI is characterized by sudden novel changes in a domain that are outside the scope of the training data, or the deployment of an agent in conditions that violate the implicit or explicit assumptions of the designer. In such situations, the AI system must detect the novelty and adapt in a short time frame. In this introduction to the special issue on open-world AI, we discuss the background and motivation for this new research area and define the field in the context of similar AI challenges. We then discuss recent research in the area that has made significant contributions to the field. Many of those contributions are reflected in the papers of this special issue, which we summarize alongside more traditional approaches to open-world AI. Finally, we discuss future directions for the field.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"347 ","pages":"Article 104393"},"PeriodicalIF":4.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621843","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":"A theory of synaptic neural balance: From local to global order","authors":"Pierre Baldi,&nbsp;Antonios Alexos,&nbsp;Ian Domingo,&nbsp;Alireza Rahmansetayesh","doi":"10.1016/j.artint.2025.104360","DOIUrl":"10.1016/j.artint.2025.104360","url":null,"abstract":"<div><div>We develop a general theory of synaptic neural balance and how it can emerge or be enforced in neural networks. For a given additive cost function <em>R</em> (regularizer), a neuron is said to be in balance if the total cost of its input weights is equal to the total cost of its output weights. The basic example is provided by feedforward networks of ReLU units trained with <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span> regularizers, which exhibit balance after proper training. The theory explains this phenomenon and extends it in several directions. The first direction is the extension to bilinear and other activation functions. The second direction is the extension to more general regularizers, including all <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> (<span><math><mi>p</mi><mo>&gt;</mo><mn>0</mn></math></span>) regularizers. The third direction is the extension to non-layered architectures, recurrent architectures, convolutional architectures, as well as architectures with mixed activation functions and to different balancing algorithms. Gradient descent on the error function alone does not converge in general to a balanced state, where every neuron is in balance, even when starting from a balanced state. However, gradient descent on the regularized error function ought to converge to a balanced state, and thus network balance can be used to assess learning progress. The theory is based on two local neuronal operations: scaling which is commutative, and balancing which is not commutative. Finally, and most importantly, given any set of weights, when local balancing operations are applied to each neuron in a stochastic manner, global order always emerges through the convergence of the stochastic balancing algorithm to the same unique set of balanced weights. The reason for this convergence is the existence of an underlying strictly convex optimization problem where the relevant variables are constrained to a linear, only architecture-dependent, manifold. Simulations show that balancing neurons prior to learning, or during learning in alternation with gradient descent steps, can improve learning speed and performance thereby expanding the arsenal of available training tools. Scaling and balancing operations are entirely local and thus physically plausible in biological and neuromorphic neural networks.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104360"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106474","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":"A semantics for probabilistic hybrid knowledge bases with function symbols","authors":"Marco Alberti ,&nbsp;Evelina Lamma ,&nbsp;Fabrizio Riguzzi ,&nbsp;Riccardo Zese","doi":"10.1016/j.artint.2025.104361","DOIUrl":"10.1016/j.artint.2025.104361","url":null,"abstract":"<div><div>Hybrid Knowledge Bases (HKBs) successfully integrate Logic Programming (LP) and Description Logics (DL) under the Minimal Knowledge with Negation as Failure semantics. Both world closure assumptions (open and closed) can be used in the same HKB, a feature required in many domains, such as the legal and health-care ones. In previous work, we proposed (function-free) Probabilistic HKBs, whose semantics applied Sato's distribution semantics approach to the well-founded HKB semantics proposed by Knorr et al. and Lyu and You. This semantics relied on the fact that the grounding of a function-free Probabilistic HKB (PHKB) is finite. In this article, we extend the PHKB language to allow function symbols, obtaining PHKB^<em>FS</em>. Because the grounding of a PHKB^<em>FS</em> can be infinite, we propose a novel semantics which does not require the PHKB^<em>FS</em>'s grounding to be finite. We show that the proposed semantics extends the previously proposed semantics and that, for a large class of PHKB^<em>FS</em>, every query can be assigned a probability.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104361"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098911","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":"CBS-Budget (CBSB): A complete and bounded suboptimal search for multi-agent path finding","authors":"Jaein Lim ,&nbsp;Panagiotis Tsiotras","doi":"10.1016/j.artint.2025.104349","DOIUrl":"10.1016/j.artint.2025.104349","url":null,"abstract":"<div><div>Multi-Agent Path Finding (MAPF) is the problem of finding a collection of conflict-free paths for a team of multiple agents while minimizing some global cost, such as the sum of the travel time of all agents, or the travel time of the last agent. Conflict Based Search (CBS) is a leading complete and optimal MAPF algorithm that lazily explores the joint agent state space, using an admissible heuristic joint plan. Such an admissible heuristic joint plan is computed by combining individual shortest paths computed without considering inter-agent conflicts, and becoming gradually more informed as constraints are added to the individual agents' path-planning problems to avoid discovered conflicts. In this paper, we seek to speed up CBS by finding a more informed heuristic joint plan that is bounded. We first propose the budgeted Class-Ordered A* (bCOA*), a novel algorithm that finds the least-cost path with the minimal number of conflicts that is upper bounded in terms of path length. Then, we propose a novel bounded-cost variant of CBS, called CBS-Budget (CBSB) by using bCOA* search at the low-level search of the CBS and by using a modified focal search at the high-level search of the CBS. We prove that CBSB is complete and bounded-suboptimal. In our numerical experiments, CBSB finds a near-optimal solution for hundreds of agents within a fraction of a second. CBSB shows state-of-the-art performance, comparable to Explicit Estimation CBS (EECBS), an enhanced recent version of CBS. On the other hand, CBSB is much easier to implement than EECBS, since only one priority queue at the low-level search is needed, as in CBS, and only two priority queues at the high-level search are needed, as in Enhanced CBS (ECBS).</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104349"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067939","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":"Active legibility in multiagent reinforcement learning","authors":"Yanyu Liu ,&nbsp;Yinghui Pan ,&nbsp;Yifeng Zeng ,&nbsp;Biyang Ma ,&nbsp;Prashant Doshi","doi":"10.1016/j.artint.2025.104357","DOIUrl":"10.1016/j.artint.2025.104357","url":null,"abstract":"<div><div>A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a <em>multiagent active legibility framework</em> to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104357"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098910","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":"NT-FAN: A simple yet effective noise-tolerant few-shot adaptation network","authors":"Wenjing Yang ,&nbsp;Haoang Chi ,&nbsp;Yibing Zhan ,&nbsp;Bowen Hu ,&nbsp;Xiaoguang Ren ,&nbsp;Dapeng Tao ,&nbsp;Long Lan","doi":"10.1016/j.artint.2025.104363","DOIUrl":"10.1016/j.artint.2025.104363","url":null,"abstract":"<div><div><em>Few-shot domain adaptation</em> (FDA) aims to train a target model with <em>clean</em> labeled data from the source domain and <em>few</em> labeled data from the target domain. Given a limited annotation budget, source data may contain many noisy labels, which can detrimentally impact the performance of models in real-world applications. This problem setting is denoted as <em>wildly few-shot domain adaptation</em> (WFDA), simultaneously taking care of label noise and data shortage. While previous studies have achieved some success, they typically rely on multiple adaptation models to collaboratively filter noisy labels, resulting in substantial computational overhead. To address WFDA more simply and elegantly, we offer a theoretical analysis of this problem and propose a comprehensive upper bound for the excess risk on the target domain. Our theoretical result reveals that correct domain-invariant representations can be obtained even in the presence of source noise and limited target data without incurring additional costs. In response, we propose a simple yet effective WFDA method, referred to as <em>noise-tolerant few-shot adaptation network</em> (NT-FAN). Experiments demonstrate that our method significantly outperforms all the state-of-the-art competitors while maintaining a more <em>lightweight</em> architecture. Notably, NT-FAN consistently exhibits robust performance when dealing with more realistic and intractable source noise (e.g., instance-dependent label noise) and severe source noise (e.g., a 40% noise rate) in the source domain.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"346 ","pages":"Article 104363"},"PeriodicalIF":5.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139326","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":"Coltrane: A domain-independent system for characterizing and planning in novel situations","authors":"Bryan Loyall ,&nbsp;Avi Pfeffer ,&nbsp;James Niehaus ,&nbsp;Michael Harradon ,&nbsp;Paola Rizzo ,&nbsp;Alex Gee ,&nbsp;Joe Campolongo ,&nbsp;Tyler Mayer ,&nbsp;John Steigerwald","doi":"10.1016/j.artint.2025.104336","DOIUrl":"10.1016/j.artint.2025.104336","url":null,"abstract":"<div><div>AI systems operating in open-world environments must be able to adapt to impactful changes in the world, immediately when they occur, and be able to do this across the many types of changes that can occur. We are seeking to create methods to extend traditional AI systems so that they can (1) immediately recognize changes in how the world works that are impactful to task accomplishment; (2) rapidly characterize the nature of the change using the limited observations that are available when the change is first detected; (3) adapt to the change as well as feasible to accomplish the system's tasks given the available observations; and (4) continue to improve the characterization and adaptation as additional observations are available. In this paper, we describe Coltrane, a domain-independent system for characterizing and planning in novel situations that uses only natural domain descriptions to generate its novelty-handling behavior, without any domain-specific anticipation of the novelty. Coltrane's characterization method is based on probabilistic program synthesis of perturbations to programs expressed in a traditional programming language describing domain transition models. Its planning method is based on incorporating novel domain models in an MCTS search algorithm and on automatically adapting the heuristics used. Both a formal external evaluation and our own demonstrations show that Coltrane is capable of accurately characterizing interesting forms of novelty and of adapting its behavior to restore its performance to pre-novelty levels and even beyond.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"345 ","pages":"Article 104336"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911499","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":"Efficient and effective budget-feasible mechanisms for submodular valuations","authors":"Kai Han ,&nbsp;Haotian Zhang ,&nbsp;Shuang Cui","doi":"10.1016/j.artint.2025.104348","DOIUrl":"10.1016/j.artint.2025.104348","url":null,"abstract":"<div><div>We revisit the classical problem of designing Budget-Feasible Mechanisms (BFMs) for submodular valuation functions, which has been extensively studied since the seminal paper of Singer [FOCS'10] due to their wide applications in crowdsourcing and social marketing. We propose <span><math><mi>TripleEagle</mi></math></span>, a novel algorithmic framework for designing BFMs, based on which we present several simple yet effective BFMs that achieve better approximation ratios than the state-of-the-art work. Moreover, our BFMs are the first in the literature to achieve linear query complexity under the value oracle model while ensuring obvious strategyproofness, making them more practical than the previous BFMs. We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results demonstrate the superiorities of our approach in terms of efficiency and effectiveness compared to the state-of-the-art BFMs.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"345 ","pages":"Article 104348"},"PeriodicalIF":5.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921715","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":"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}