Artificial Intelligence最新文献_第5页

Learngene: Inheritable “genes” in intelligent agents Learngene：智能体中可遗传的“基因”

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-11-01 Epub Date: 2025-09-22 DOI: 10.1016/j.artint.2025.104421

Fu Feng , Jing Wang , Xu Yang , Xin Geng

{"title":"Learngene: Inheritable “genes” in intelligent agents","authors":"Fu Feng , Jing Wang , Xu Yang , Xin Geng","doi":"10.1016/j.artint.2025.104421","DOIUrl":"10.1016/j.artint.2025.104421","url":null,"abstract":"<div><div>Biological intelligence has driven significant progress in artificial intelligence (AI), but a critical gap remains: biological systems inherit innate abilities from genes, with brains initialized by blueprints refined over 3.5 billion years of evolution, while machines rely heavily on inefficient, data-driven learning from scratch. This gap arises from the lack of a genetic mechanism in machines to transfer and accumulate inheritable knowledge across generations. To bridge this gap, we propose learngenes, network fragments that act as inheritable “genes” for machines. Unlike conventional knowledge transfer methods, learngenes enable efficient and universal knowledge transfer by selectively encapsulating task-agnostic knowledge. To facilitate the transfer and accumulation of task-agnostic knowledge across generations, we introduce Genetic Reinforcement Learning (GRL), a framework that simulates the learning and evolution of organisms in intelligent agents following Lamarckian principles. Through GRL, we identify learngenes as network fragments within agents' policy networks, equipping newborn agents with innate abilities for rapid adaptation to novel tasks. We demonstrate the advantages of learngene-based knowledge transfer over evolution-based search and traditional pre-trained models, and show how learngenes evolve through the accumulation of task-agnostic knowledge. Overall, this work establishes a novel paradigm for knowledge transfer and model initialization in AI, offering new possibilities for more adaptive, efficient, and scalable learning systems.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104421"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145154766","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 : 2025-11-01 Epub 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":"2025-11-01","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

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 : 2025-11-01 Epub 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":"2025-11-01","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

Provably efficient information-directed sampling algorithms for multi-agent reinforcement learning 多智能体强化学习中可证明的高效信息导向采样算法

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-11-01 Epub Date: 2025-07-10 DOI: 10.1016/j.artint.2025.104392

Qiaosheng Zhang , Chenjia Bai , Shuyue Hu , Zhen Wang , Xuelong Li

{"title":"Provably efficient information-directed sampling algorithms for multi-agent reinforcement learning","authors":"Qiaosheng Zhang , Chenjia Bai , Shuyue Hu , Zhen Wang , Xuelong Li","doi":"10.1016/j.artint.2025.104392","DOIUrl":"10.1016/j.artint.2025.104392","url":null,"abstract":"<div><div>This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling (IDS). These algorithms draw inspiration from foundational concepts in information theory, and are proven to be sample efficient in MARL settings such as two-player zero-sum Markov games (MGs) and multi-player general-sum MGs. For episodic two-player zero-sum MGs, we present three sample-efficient algorithms for learning Nash equilibrium. The basic algorithm, referred to as <span>MAIDS</span>, employs an asymmetric learning structure where the max-player first solves a minimax optimization problem based on the <em>joint information ratio</em> of the joint policy, and the min-player then minimizes the <em>marginal information ratio</em> with the max-player's policy fixed. Theoretical analyses show that it achieves a Bayesian regret of <span><math><mover><mrow><mi>O</mi></mrow><mrow><mo>˜</mo></mrow></mover><mo>(</mo><msqrt><mrow><mi>K</mi></mrow></msqrt><mo>)</mo></math></span> for <em>K</em> episodes. To reduce the computational load of <span>MAIDS</span>, we develop an improved algorithm called <span>Reg-MAIDS</span>, which has the same Bayesian regret bound while enjoying less computational complexity. Moreover, by leveraging the flexibility of IDS principle in choosing the learning target, we propose two methods for constructing compressed environments based on rate-distortion theory, upon which we develop an algorithm <span>Compressed-MAIDS</span> wherein the learning target is a compressed environment. Finally, we extend <span>Reg-MAIDS</span> to multi-player general-sum MGs and prove that it can learn either the Nash equilibrium or coarse correlated equilibrium in a sample-efficient manner.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104392"},"PeriodicalIF":5.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613978","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 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 : 2025-11-01 Epub 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":"2025-11-01","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

BATED: Learning fair representation for Pre-trained Language Models via biased teacher-guided disentanglement BATED：通过有偏见的教师引导的解纠缠来学习预训练语言模型的公平表示

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-11-01 Epub Date: 2025-08-08 DOI: 10.1016/j.artint.2025.104401

Yingji Li , Mengnan Du , Rui Song , Mu Liu , Ying Wang

{"title":"BATED: Learning fair representation for Pre-trained Language Models via biased teacher-guided disentanglement","authors":"Yingji Li , Mengnan Du , Rui Song , Mu Liu , Ying Wang","doi":"10.1016/j.artint.2025.104401","DOIUrl":"10.1016/j.artint.2025.104401","url":null,"abstract":"<div><div>With the rapid development of Pre-trained Language Models (PLMs) and their widespread deployment in various real-world applications, social biases of PLMs have attracted increasing attention, especially the fairness of downstream tasks, which potentially affects the development and stability of society. Among existing debiasing methods, intrinsic debiasing methods are not necessarily effective when applied to downstream tasks, and the downstream fine-tuning process may introduce new biases or catastrophic forgetting. Most extrinsic debiasing methods rely on sensitive attribute words as prior knowledge to supervise debiasing training. However, it is difficult to collect sensitive attribute information of real data due to privacy and regulation. Moreover, limited sensitive attribute words may lead to inadequate debiasing training. To this end, this paper proposes a debiasing method to learn fair representation for PLMs via <strong>B</strong>i<strong>A</strong>sed <strong>TE</strong>acher-guided <strong>D</strong>isentanglement (called <strong>BATED</strong>). Specific to downstream tasks, BATED performs debiasing training under the guidance of a biased teacher model rather than relying on sensitive attribute information of the training data. First, we leverage causal contrastive learning to train a task-agnostic general biased teacher model. We then employ Variational Auto-Encoder (VAE) to disentangle the PLM-encoded representation into the fair representation and the biased representation. The Biased representation is further decoupled via biased teacher-guided disentanglement, while the fair representation learn downstream tasks. Therefore, BATED guarantees the performance of downstream tasks while improving the fairness. Experimental results on seven PLMs testing three downstream tasks demonstrate that BATED outperforms the state-of-the-art overall in terms of fairness and performance on downstream tasks.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104401"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810517","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

Bridging theory and practice in bidirectional heuristic search with front-to-end consistent heuristics 基于前端一致性启发式的双向启发式搜索理论与实践的桥梁

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-11-01 Epub Date: 2025-09-19 DOI: 10.1016/j.artint.2025.104420

Lior Siag, Shahaf S. Shperberg

引用次数: 0

Unsupervised sentence selection for creating a representative corpus in Turkish: An active learning approach 创建土耳其语代表性语料库的无监督句子选择：一种主动学习方法

IF 4.6 2区计算机科学

Artificial Intelligence Pub Date : 2025-11-01 Epub Date: 2025-09-22 DOI: 10.1016/j.artint.2025.104422

Hayri Volkan Agun

{"title":"Unsupervised sentence selection for creating a representative corpus in Turkish: An active learning approach","authors":"Hayri Volkan Agun","doi":"10.1016/j.artint.2025.104422","DOIUrl":"10.1016/j.artint.2025.104422","url":null,"abstract":"<div><div>In this study, active learning methods adapted for sentence selection of Turkish sentences are evaluated through language learning with neural models. Turkish is an agglutinative language with a complex morphology, where the linguistic properties of words are encoded in suffixes. The active learning methods based on regression, clustering, language models, distance metrics, and neural networks are applied to unlabeled sentence selection. In this respect, a sentence corpus is selected from a larger corpus, with the same number of samples for each target word in intrinsic and extrinsic evaluation tasks. The selected sentences are used for the training of SkipGram, CBOW, and self-attention LSTM language models and extracted embeddings are evaluated by the semantic analogy, POS and sentiment analysis tasks. The evaluation scores of the models trained on the samples selected by the active learning method are compared. The results of the selected sentences based on language models indicate an improvement over random selection based on a static vocabulary. These results also show that the selection affects the quality of unsupervised word embedding extraction even if the target vocabulary is kept the same. Along with the accuracy, the time efficiency of the language models is shown to be better than other methods especially methods based on neural network models, and distance metrics.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"348 ","pages":"Article 104422"},"PeriodicalIF":4.6,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145104222","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 : 2025-11-01 Epub 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":"2025-11-01","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

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 : 2025-11-01 Epub 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":"2025-11-01","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