Artificial Intelligence最新文献_第2页

On the disjunctive rational closure of a conditional knowledge base 论条件知识库的析取理性闭包

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-12 DOI: 10.1016/j.artint.2025.104418

Richard Booth , Ivan Varzinczak

{"title":"On the disjunctive rational closure of a conditional knowledge base","authors":"Richard Booth , Ivan Varzinczak","doi":"10.1016/j.artint.2025.104418","DOIUrl":"10.1016/j.artint.2025.104418","url":null,"abstract":"<div><div>One of the most widely investigated decision problems in symbolic AI is that of which conditional sentences of the form “if <em>α</em>, then normally <em>β</em>” should follow from a knowledge base containing this type of statements. Probably, the most notable approach to this problem is the rational closure construction put forward by Lehmann and Magidor in the'90s, which has been adapted to logical languages of various expressive powers since then. At the core of rational closure is the Rational Monotonicity property, which allows one to retain existing (defeasible) conclusions whenever new information cannot be negated by existing conclusions. As it turns out, Rational Monotonicity is not universally accepted, with many researchers advocating the investigation of weaker versions thereof leading to a larger class of consequence relations. A case in point is that of the Disjunctive Rationality property, which states that if one may draw a (defeasible) conclusion from a disjunction of premises, then one should be able to draw this conclusion from at least one of the premises taken alone. While there are convincing arguments that the rational closure forms the ‘simplest’ rational consequence relation extending a given set of conditionals, the question of what the simplest disjunctive consequence relation in this setting is has not been explored in depth. In this article, we do precisely that by motivating and proposing a concrete construction of the disjunctive rational closure of a conditional knowledge base, of which the properties and consequences of its adoption we also investigate in detail. (Previous versions of this work have been selected for presentation at the 18th International Workshop on Nonmonotonic Reasoning (NMR 2020) <span><span>[1]</span></span> and at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021) <span><span>[2]</span></span>. The present submission extends and elaborates on both papers.)</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104418"},"PeriodicalIF":4.6,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094874","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}

引用次数: 0

Rethinking visual prompt learning as masked visual token modeling 视觉提示学习作为蒙面视觉标记建模的再思考

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-10 DOI: 10.1016/j.artint.2025.104417

Ning Liao , Bowen Shi , Xiaopeng Zhang , Min Cao , Junchi Yan , Qi Tian

{"title":"Rethinking visual prompt learning as masked visual token modeling","authors":"Ning Liao , Bowen Shi , Xiaopeng Zhang , Min Cao , Junchi Yan , Qi Tian","doi":"10.1016/j.artint.2025.104417","DOIUrl":"10.1016/j.artint.2025.104417","url":null,"abstract":"<div><div>Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as the generative pre-training ones to achieve consistency, thus improving the performance stably. However, when transferring it to the vision area, current visual prompt learning methods are almost designed on discriminative pre-trained models, and there is also a lack of careful design to unify the forms of pre-training and downstream tasks. To explore prompt learning on the generative pre-trained visual model, as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification task into the pre-trained masked visual token prediction task. In addition, we develop the prototypical verbalizer for mapping the predicted visual token with implicit semantics to explicit downstream labels. To our best knowledge, VPTM is the first visual prompt method on the generative pre-trained visual model, which achieves consistency between pre-training and downstream visual classification by task reformulation. Experiments show that VPTM outperforms other visual prompt methods and achieves excellent efficiency. Moreover, the task consistency of VPTM contributes to the robustness against prompt location, prompt length and prototype dimension, and could be deployed uniformly.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104417"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044399","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}

引用次数: 0

Centralized training with hybrid execution in multi-agent reinforcement learning via predictive observation imputation 基于预测观测插值的多智能体强化学习集中训练混合执行

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-10 DOI: 10.1016/j.artint.2025.104404

Pedro P. Santos , Diogo S. Carvalho , Miguel Vasco , Alberto Sardinha , Pedro A. Santos , Ana Paiva , Francisco S. Melo

{"title":"Centralized training with hybrid execution in multi-agent reinforcement learning via predictive observation imputation","authors":"Pedro P. Santos , Diogo S. Carvalho , Miguel Vasco , Alberto Sardinha , Pedro A. Santos , Ana Paiva , Francisco S. Melo","doi":"10.1016/j.artint.2025.104404","DOIUrl":"10.1016/j.artint.2025.104404","url":null,"abstract":"<div><div>We study <em>hybrid execution</em> in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104404"},"PeriodicalIF":4.6,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060254","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}

引用次数: 0

Planning for temporally extended goals in pure-past linear temporal logic 在纯过去线性时间逻辑中规划时间扩展目标

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-08 DOI: 10.1016/j.artint.2025.104409

Luigi Bonassi , Giuseppe De Giacomo , Marco Favorito , Francesco Fuggitti , Alfonso Emilio Gerevini , Enrico Scala

{"title":"Planning for temporally extended goals in pure-past linear temporal logic","authors":"Luigi Bonassi , Giuseppe De Giacomo , Marco Favorito , Francesco Fuggitti , Alfonso Emilio Gerevini , Enrico Scala","doi":"10.1016/j.artint.2025.104409","DOIUrl":"10.1016/j.artint.2025.104409","url":null,"abstract":"<div><div>We study planning for temporally extended goals expressed in Pure-Past Linear Temporal Logic (<span>ppltl</span>) in the context of deterministic (i.e., classical) and fully observable nondeterministic (FOND) domains. <span>ppltl</span> is the variant of Linear-time Temporal Logic on finite traces (<span>ltl</span><sub><em>f</em></sub>) that refers to the past rather than the future. Although <span>ppltl</span> is as expressive as <span>ltl</span><sub><em>f</em></sub>, we show that it is computationally much more effective for planning. In particular, we show that checking the validity of a plan for a <span>ppltl</span> formula is Markovian. This is achieved by introducing a linear number of additional propositional variables that capture the validity of the entire formula in a modular fashion. The solution encoding introduces only a linear number of new fluents proportional to the size of the <span>ppltl</span> goal and does not require any additional spurious action. We implement our solution technique in a system called <span><math><mi>Plan4Past</mi></math></span>, which can be used alongside state-of-the-art classical and FOND planners. Our empirical analysis demonstrates the practical effectiveness of <span><math><mi>Plan4Past</mi></math></span> in both classical and FOND problems, showing that the resulting planner performs overall better than other planning approaches for <span>ltl</span><sub><em>f</em></sub> goals.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104409"},"PeriodicalIF":4.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145044398","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}

引用次数: 0

Incentives for responsiveness, instrumental control and impact 激励响应，工具控制和影响

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-02 DOI: 10.1016/j.artint.2025.104408

Ryan Carey , Eric Langlois , Chris van Merwijk , Shane Legg , Tom Everitt

引用次数: 0

Abstracting situation calculus action theories 抽象情境演算行动理论

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-09-01 DOI: 10.1016/j.artint.2025.104407

Bita Banihashemi , Giuseppe De Giacomo , Yves Lespérance

{"title":"Abstracting situation calculus action theories","authors":"Bita Banihashemi , Giuseppe De Giacomo , Yves Lespérance","doi":"10.1016/j.artint.2025.104407","DOIUrl":"10.1016/j.artint.2025.104407","url":null,"abstract":"<div><div>We develop a general framework for <em>agent abstraction</em> based on the situation calculus and the <span>ConGolog</span> agent programming language. We assume that we have a high-level specification and a low-level specification of the agent, both represented as basic action theories. A <em>refinement mapping</em> specifies how each high-level action is implemented by a low-level <span>ConGolog</span> program and how each high-level fluent can be translated into a low-level formula. We define a notion of <em>sound abstraction</em> between such action theories in terms of the existence of a suitable bisimulation between their respective models. Sound abstractions have many useful properties that ensure that we can reason about the agent's actions (e.g., executability, projection, and planning) at the abstract level, and refine and concretely execute them at the low level. We also characterize the notion of <em>complete abstraction</em> where all actions (including exogenous ones) that the high level thinks can happen can in fact occur at the low level. To facilitate verifying that one has a sound/complete abstraction relative to a mapping, we provide a set of necessary and sufficient conditions. Finally, we identify a set of basic action theory constraints that ensure that for any low-level action sequence, there is a unique high-level action sequence that it refines. This allows us to track/monitor what the low-level agent is doing and describe it in abstract terms (i.e., provide high-level explanations, for instance, to a client or manager).</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104407"},"PeriodicalIF":4.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144988707","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}

引用次数: 0

On preference learning based on sequential Bayesian optimization with pairwise comparison 基于两两比较序列贝叶斯优化的偏好学习

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-08-28 DOI: 10.1016/j.artint.2025.104400

Tanya Ignatenko , Kirill Kondrashov , Marco Cox , Bert de Vries

{"title":"On preference learning based on sequential Bayesian optimization with pairwise comparison","authors":"Tanya Ignatenko , Kirill Kondrashov , Marco Cox , Bert de Vries","doi":"10.1016/j.artint.2025.104400","DOIUrl":"10.1016/j.artint.2025.104400","url":null,"abstract":"<div><div>User preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, one representing a user with his/her preferences and another one representing an agent that has to learn these preferences. The user with his/her behavior is modeled by a parametric preference function. To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning. The agent presents two proposals to the user for evaluation, and the user rates them based on his/her preference function. We show that the optimum agent strategy for data collection and preference learning is a result of maximin optimization of the normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions. The resulting value of the KL-divergence, which we also call of a remaining system uncertainty (RSU), provides an efficient performance metric in the absence of the ground truth. This metric characterizes how well the agent can predict user and, thus, the quality of the underlying learned user (preference) model. Our proposed agent comprises sequential mechanisms for user model inference and proposal generation. To infer the user model (preference function), Bayesian approximate inference is used in the agent. The data collection strategy is to generate proposals, responses to which help resolving uncertainty associated with prediction of the user responses the most. The efficiency of our approach is validated by numerical simulations. Also a real-life example of preference learning application is provided.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104400"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145018407","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}

引用次数: 0

Towards optimal subsidy bounds for envy-freeable allocations 无嫉妒分配的最优补贴界限

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-08-28 DOI: 10.1016/j.artint.2025.104406

Yasushi Kawase , Kazuhisa Makino , Hanna Sumita , Akihisa Tamura , Makoto Yokoo

{"title":"Towards optimal subsidy bounds for envy-freeable allocations","authors":"Yasushi Kawase , Kazuhisa Makino , Hanna Sumita , Akihisa Tamura , Makoto Yokoo","doi":"10.1016/j.artint.2025.104406","DOIUrl":"10.1016/j.artint.2025.104406","url":null,"abstract":"<div><div>We study the fair division of indivisible items with subsidies among <em>n</em> agents, where the absolute marginal valuation of each item is at most one. Under monotone nondecreasing valuations (where each item is a good), Brustle et al. <span><span>[9]</span></span> demonstrated that a maximum subsidy of <span><math><mn>2</mn><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></math></span> and a total subsidy of <span><math><mn>2</mn><msup><mrow><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo></mrow><mrow><mn>2</mn></mrow></msup></math></span> are sufficient to guarantee the existence of an envy-freeable allocation. In this paper, we improve upon these bounds, even in a wider model. Namely, we show that, given an EF1 allocation, we can compute in polynomial time an envy-free allocation with a subsidy of at most <span><math><mi>n</mi><mo>−</mo><mn>1</mn></math></span> per agent and a total subsidy of at most <span><math><mi>n</mi><mo>(</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo><mo>/</mo><mn>2</mn></math></span>. Moreover, when the valuations are monotone nondecreasing, we provide a polynomial-time algorithm that computes an envy-free allocation with a subsidy of at most <span><math><mi>n</mi><mo>−</mo><mn>1.5</mn></math></span> per agent and a total subsidy of at most <span><math><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>−</mo><mi>n</mi><mo>−</mo><mn>1</mn><mo>)</mo><mo>/</mo><mn>2</mn></math></span>.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104406"},"PeriodicalIF":4.6,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912200","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}

引用次数: 0

Local-MIP: Efficient local search for mixed integer programming local - mip：混合整数规划的高效局部搜索

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-08-27 DOI: 10.1016/j.artint.2025.104405

Peng Lin , Shaowei Cai , Mengchuan Zou , Jinkun Lin

{"title":"Local-MIP: Efficient local search for mixed integer programming","authors":"Peng Lin , Shaowei Cai , Mengchuan Zou , Jinkun Lin","doi":"10.1016/j.artint.2025.104405","DOIUrl":"10.1016/j.artint.2025.104405","url":null,"abstract":"<div><div>Mixed Integer Programming (MIP) is a fundamental model in operations research with broad industrial applications. Local search is a powerful methodology for solving complex optimization problems; however, the development of local search algorithms for MIP still needs exploration. In this work, we propose <em>Local-MIP</em>, an efficient local search algorithm tailored for MIP that integrates novel operators and employs a two-mode architecture to adaptively apply operators based on the current solution's feasibility. For the feasible mode, we propose the lift move operator and a corresponding lift process to improve the objective value while maintaining feasibility. For the infeasible mode, we propose the breakthrough move and mixed tight move operators to respectively optimize the objective function and satisfy constraints. To apply operators intelligently, we develop a dynamic weighting scheme that balances the priorities of the objective function and constraints. Furthermore, we propose a two-level scoring function structure that hierarchically selects operations, guiding the search toward high-quality feasible solutions. Experiments are conducted on public benchmarks to compare <em>Local-MIP</em> with state-of-the-art MIP solvers in finding high-quality solutions. The results show that <em>Local-MIP</em> significantly outperforms <em>CPLEX</em>, <em>HiGHS</em>, <em>SCIP</em>, and <em>Feasibility Jump</em> while remaining competitive with the commercial solver <em>Gurobi</em> on challenging problems within short time limits. Moreover, <em>Local-MIP</em> establishes 10 new records on MIPLIB open instances.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104405"},"PeriodicalIF":4.6,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922750","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}

引用次数: 0

Algebras of actions in an agent's representations of the world 代理对世界的表示中的行动代数

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-08-20 DOI: 10.1016/j.artint.2025.104403

Alexander Dean, Eduardo Alonso, Esther Mondragón

{"title":"Algebras of actions in an agent's representations of the world","authors":"Alexander Dean, Eduardo Alonso, 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":"2025-08-20","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}

引用次数: 0