A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
C$^{2}$DL, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所C²DL实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；University of Science and Technology of China（中国科学技术大学）；Qwen Team, Alibaba Group（阿里集团Qwen团队）
Symmetry-Breaking Augmentations for Ad Hoc Teamwork
Foerster Lab for AI Research, University of Oxford（牛津大学人工智能研究实验室）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学人工智能研究所）；Meta AI Research, UK（英国Meta人工智能研究）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Bar Ilan University（巴伊兰大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；The University of Edinburgh（爱丁堡大学）；The University of Birmingham（伯明翰大学）；Sony AI（索尼人工智能）
Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observability
INESC-ID（INESC-ID（高等工程系统与计算机研究所））；Instituto Superior Técnico, University of Lisbon（里斯本大学高等技术学院）；Pontifícia Universidade Católica do Rio de Janeiro（里约热内卢天主教大学）
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
Department of Computer Science（计算机科学系）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；College of Computing and Data Science（计算与数据科学学院）；Nanyang Technological University（南洋理工大学）
Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning
NEC Laboratories Europe（NEC欧洲实验室）；Université Paris-Saclay（巴黎萨克雷大学）；CentraleSupélec（中央理工-高等电力学院）；University Carlos III of Madrid（马德里卡洛斯三世大学）；Flyhound Co.（Flyhound公司）；i2cat Foundation（i2cat基金会）；ICREA（加泰罗尼亚研究与高级研究院）
Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Imperial College London, Department of Civil and Environmental Engineering（帝国理工学院伦敦校区土木与环境工程系）；Imperial College London, Department of Computing（帝国理工学院伦敦校区计算机系）
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Poznan University of Technology（波兹南理工大学）；Technische Universität Darmstadt（达姆施塔特工业大学）；Huawei R&D London（华为伦敦研发中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Institute of Technology（北京理工大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）
Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Amsterdam（阿姆斯特丹大学）
