Research on multi-agent planning has been popular in recent years. While previous research has been motivated by the understanding that, through cooperation, multi-agent systems can achieve tasks that are unachievable by single-agent systems, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multi-agent systems. In this paper, we provide such a formal discussion from the planning aspect. We first show that determining whether there is required cooperation (RC) is intractable is general. Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes. We establish that when none of these identified conditions hold, the problem is single-agent solvable. Furthermore, with a few assumptions, we provide an upper bound on the minimum number of agents required for RC problems with homogeneous agents. This study not only provides new insights into multi-agent planning, but also has many applications. For example, in human-robot teaming, when a robot cannot achieve a task, it may be due to RC. In such cases, the human teammate should be informed and, consequently, coordinate with other available robots for a solution.
Improving Multi-Agent Cooperation using Theory of Mind
National University of Singapore（新加坡国立大学）；AI Singapore（新加坡人工智能计划）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究院）
COMBO: Compositional World Models for Embodied Multi-Agent Cooperation
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IIIS, Tsinghua University（清华大学信息科学技术学院）；Peking University（北京大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Honda Research Institute USA（美国本田研究所）；MIT（麻省理工学院）
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments
The Hong Kong University of Science and Technology（香港科技大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；East China University of Science and Technology（华东理工大学）
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network
University of Dhaka（达卡大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
多智能体马尔可夫决策过程（MMDP）已成为一种有效的建模方法的顺序决策算法的多智能体合作环境。在这一领域已经开发了许多基于集中式和分散式规划的算法。然而，动态变化的环境，加上指数大小的状态和联合行动空间，使这些算法难以提供效率和可扩展性。近年来，集中式规划算法FV-MCTS-MP和分散式规划算法\textit{Alternate maximization with Behavioural Cloning}（ABC）在求解MMDPs问题上取得了显著的性能。然而，它们不能适应动态变化的环境和会计代理之间缺乏沟通，分别。在此背景下，我们介绍了一个模拟为基础的在线规划算法，我们称之为SiCLOP，多智能体协作环境。具体来说，SiCROP定制蒙特卡罗树搜索（MCTS），并使用协调图（CG）和图神经网络（GCN）来学习合作，并提供MMDP问题的实时的解决方案。它还通过有效修剪动作空间来提高可伸缩性。此外，与FV-MCTS-MP和ABC不同，SiCROP支持迁移学习，这使得学习过的代理能够在不同的环境中运行。我们还提供了理论上的讨论，我们的算法的收敛性的背景下，多智能体设置。最后，我们广泛的实证结果表明，SiCLOP显着优于国家的最先进的在线规划算法。
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond
Sichuan University（四川大学）；Singapore Management University（新加坡国立大学）；York University（约克大学）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China（机器学习与产业智能工程研究中心，教育部，中国）；National University of Singapore（新加坡国立大学）
Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
School of Computing, Clemson University（克莱姆森大学计算学院）；California State University, Los Angeles（加州州立大学洛杉矶分校）；Lincoln Laboratory, Massachusetts Institute of Technology（麻省理工学院林肯实验室）
VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；University of Oxford（牛津大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge
Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；University of California, Santa Barbara（加州大学圣芭芭拉分校）；Harvard University（哈佛大学）；University of Massachusetts Amherst（马萨诸塞大学阿姆赫斯特分校）；MIT（麻省理工学院）；Johns Hopkins University（约翰霍普金斯大学）；Honda Research Institute USA（本田美国研究院）
Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback
Department of Sociology, University of Zurich（苏黎世大学社会学系）；Centre for Sociology of Humans and Machines (SOHAM), Trinity College Dublin & Technological University Dublin（人类与机器社会学中心（SOHAM））
