Lifted Causal Inference
提升因果推断
机构 * German Research Center for Artificial Intelligence (DFKI)(德国人工智能研究中心)
AI总结 提出参数化因果因子图(PCFG)和提升因果推断(LCI)算法,在关系域中高效计算因果效应,显著加速推理。
Comments Accepted to the Annals of Mathematics and Artificial Intelligence journal
期刊&会议
Artificial Intelligence Journal · 期刊 · Artificial Intelligence
提升因果推断
机构 * German Research Center for Artificial Intelligence (DFKI)(德国人工智能研究中心)
AI总结 提出参数化因果因子图(PCFG)和提升因果推断(LCI)算法,在关系域中高效计算因果效应,显著加速推理。
Comments Accepted to the Annals of Mathematics and Artificial Intelligence journal
基于LLM的异构机器人团队通用分层任务规划与执行及事件驱动重规划
机构 * Department of Mechanical Engineering, IIT Gandhinagar(印度加尔各直辖区理工学院机械工程系) ; Sahyadri College of Engineering and Management(萨哈亚德里工程与管理学院) ; Vishwakarma Institute of Information Technology(维什瓦克arma信息技术学院)
AI总结 提出CoMuRoS架构,结合集中式规划与分布式执行,通过LLM解释自然语言目标、分配子任务,并支持事件驱动重规划,在硬件实验中实现自主恢复和协调运输。
Comments full version of this short paper is accepted at Frontiers in Robotics and AI Journal
从Medline数据库中结合领域知识发现概念间的新连接
AI总结 提出一种基于Swanson ABC模型的改进自适应模型,用于文献发现中隐藏的概念连接,通过中间主题B连接看似无关的主题A和C。
Journal ref Artificial Intelligence, IntechOpen, 2024
多目标进化算法在冲突程度不同问题中的优势证明
AI总结 本文通过对比不同冲突程度的问题,证明多目标进化算法在解决OneMaxMin_k问题时优于传统方法,且在不同冲突程度下均能高效求解。
Comments This is the preprint version of an article accepted for publication in Artificial Intelligence Journal
Journal ref Artificial Intelligence, 2026, 104573
面向可持续电动汽车充电与二氧化碳减排的排放感知强化学习:在不同可再生能源渗透率下
机构 * organization= nasc Research, School of Computer Science \& IT, University College Cork , country = IE ; organization= School of Computing, Engineering ; Digital Technologies, Teesside University , country= UK ; organization= International Energy Research Centre, Tyndall National Institute, Cork , country = IE ; Radiation Technologies Group, CCDCU, Faculty of Engineering ; Technology, Sunway University , country = Malaysia ; organization= Department of Physics, College of Science, Korea University , country = Republic of Korea
AI总结 提出基于软演员-评论家算法的排放感知强化学习策略,通过多目标奖励函数优化电动汽车充电调度,在EV2Gym平台上实现高达87%的碳排放减少和52%的可再生能源自消纳率。
Comments Submitted the Engineering Applications of Artificial Intelligence Journal (Elsevier)
贝叶斯马尔可夫决策过程的离线风险感知策略选择方法
机构 * Natural Intelligence Toulouse Institute, University of Toulouse, France(图卢兹大学自然智能研究所) ; ISAE-SUPAERO, University of Toulouse, France(图卢兹大学ISAE-SUPAERO)
AI总结 针对离线强化学习中模型不确定性导致策略风险高的问题,提出一种基于贝叶斯形式化框架的风险感知策略选择方法EvC,通过最大化贝叶斯后验下的风险感知目标来选择稳健策略。
Comments Preprint, under review
Journal ref Artificial Intelligence, Volume 354, 2026
任何人但 him:排除替代品的复杂性
AI总结 本文研究了在多智能体环境下,通过机制如选民/候选人添加/删除/分割等,防止特定候选人获胜的复杂性问题,发现不同选举系统对破坏性控制的防护能力各不相同。
Comments This revision--the March 2026 Version 5--is identical to the March 2006 Version 4 except in providing, as Appendix A, a correction to the second half of the proof of Theorem 4.21 as it appears in both Version 4 and the AIJ journal version; this proof also replaces the analogous proof part of Theorem 6 of the AAAI version
当梯度裁剪成为深度学习中差分隐私的控制机制
机构 * Computer Science, University of California, Merced, CA 95343, USA(计算机科学,加州大学默塞德分校) ; Department of Applied Mathematics, University of California, Merced, CA 95343, USA(应用数学系,加州大学默塞德分校) ; Department of Innovation(创新部门) ; Research, North Carolina State University, Raleigh, NC, USA(研究,北卡罗来纳州立大学,拉洛城) ; Automation (MESA) Lab, Department of Mechanical Engineering, School of Engineering, University of California, Merced, CA 95343, USA(自动化(MESA)实验室,机械工程系,工程学院,加州大学默塞德分校)
AI总结 本文提出了一种基于控制机制的梯度裁剪策略,通过模型参数的谱诊断动态调整裁剪阈值,以在差分隐私训练中平衡隐私保护与模型性能。
Comments This manuscript is under review in the Engineering Applications of Artificial Intelligence journal
具有中间条件和效果的符号模式时间数字规划
AI总结 本文提出了一种扩展的符号模式规划方法,用于处理具有中间条件和效果的时间规划问题,实验显示其在多个领域表现优异。
Comments Under review at the Artificial Intelligence Journal
多激发投影模拟:受多体物理启发的归纳偏置
机构 * University of Innsbruck, Institute for Theoretical Physics(因斯布鲁克大学理论物理研究所)
AI总结 本文提出多激发投影模拟(mePS)方法,通过受多体物理启发的归纳偏置解决超图建模中的指数复杂性问题,并在多个场景中验证了其资源节省和可解释性优势。
Comments 41 pages, 9 figures; Code repository at https://github.com/MariusKrumm/ManyBodyMEPS. Updated to be consistent with AIJ version
Journal ref Artificial Intelligence 352, 104489 (2026)
COL-Trees: 路网中高效的层次对象搜索
机构 * Center for Computational Science, RIKEN(计算科学中心,RIKEN) ; Faculty of Information Technology, Monash University(信息科技学院,墨尔本大学) ; Department of Computer and Information Science, University of Konstanz(计算机与信息科学系,康斯坦茨大学)
AI总结 COL-Trees通过高效的层次图遍历方法,提升了道路网络中多代理POI搜索的效率,实现高达4个数量级的性能改进。
Comments Submitted to Artificial Intelligence (AIJ)
解决得分问题:基于基数偏好的分割
AI总结 本文研究了基于基数偏好的分割问题,提出通过计算提案平均值的简单规则,在公平性方面表现优异,并从公理学角度进行了详细分析。
Comments A preliminary version appeared in the 26th European Conference on Artificial Intelligence (ECAI), 2023
Journal ref Artificial Intelligence, 352:104487 (2026)
在本体存在的情况下SHACL验证的语义与重写技术
AI总结 本文提出了一种基于核心普遍模型的SHACL验证语义,利用描述逻辑Horn-ALCHIQ构建本体模型,并通过重写技术将本体存在下的SHACL验证转化为标准验证,证明其复杂性为EXPTIME完全问题。
Comments Published in AIJ
Journal ref Volume 352, 2026, 104483
联邦神经非参数点过程
机构 * Macquarie University(麦考瑞大学) ; Aalborg University(奥胡斯大学) ; Renmin University of China(中国人民大学) ; Iowa State University(爱荷华州立大学)
AI总结 FedPP通过整合神经嵌入和SGCPs,有效解决联邦系统中稀疏和不确定事件建模问题,提升隐私保护与个性化性能。
Journal ref Artificial Intelligence, vol. 351, 104454, 2026
Comments This paper unifies and extends preliminary versions that appeared in AAAI 2024 (DOI:10.1609/aaai.v38i18.30015) and WINE 2024 (arXiv:2407.15261v1)
Journal ref Artificial Intelligence, Vol. 349, 104426, 2025
机构 * University of Amsterdam(阿姆斯特丹大学)
Comments This paper has been published in Neurosymbolic Artificial Intelligence Journal (SAGE)
机构 * University of Coimbra(科英布拉大学) ; Institute for Biomedical Imaging and Translational Research(生物医学影像与转化研究 institutes) ; Institute of Systems and Robotics(系统与机器人研究所)
Comments 17 pages, 8 figures, MDPI's AI Journal
Journal ref Assuncao, G.; Castelo-Branco, M.; Menezes, P. Self-Emotion-Mediated Exploration in Artificial Intelligence Mirrors: Findings from Cognitive Psychology. AI 2025, 6, 220
机构 * Department One(部门一) ; Department Two(部门二) ; Department of Mathematics, University of Padova(帕多瓦大学数学系) ; Department of General Psychology, University of Padova(帕多瓦大学普通心理学系) ; Augmented Intelligence Center, Fondazione Bruno Kessler(布鲁诺·凯塞勒基金会增强智能中心)
Comments 48 pages, 31 figures. Submitted for review by Artificial Intelligence Journal
机构 * Research and Innovation Lab, Novelis(创新研究实验室,Novelis)
Comments Preprint submitted to Engineering Applications of Artificial Intelligence journal
Comments This paper was submitted to the Frontiers in Robotics and AI journal on the 22/02/2020, and is still under review
Comments Appears in the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023
Journal ref Artificial Intelligence, 347:104385 (2025)
Comments 47 pages, 12 figures, 4 table, submission to Energy and AI journal
Journal ref Artificial Intelligence, Entrepreneurship and Risk. Springer, 2025
Comments Explanation for practical implementation of cardinality constraints added in Appendix .arXiv admin note: text overlap with arXiv:2302.09830
Journal ref Artificial Intelligence,Volume 342,2025
Comments Accepted by Artificial Intelligence Journal. A preliminary version of this paper has appeared at IJCAI'23
Comments 44 Pages. 1stversion was submitted on Artificial Intelligence Journal, Jan 29, 2024, ARTINT-D-24-00098
Journal ref Artificial Intelligence Volume 337, December 2024, 104229
Comments This is the pre-print. The article is submitted to the Engineering Applications of Artificial Intelligence journal
Comments 22 pages, 15 figures, submitting to a AI Journal
Comments The original version was published in the Artificial Intelligence journal. This original version uses 'justifications' in the proof system, which we would call nowadays 'arguments'. The current version presents the same results but now using the terminology of an assumption-based argumentation system
Journal ref Artificial Intelligence 57 (1992) 69-103