Brigham Young University（杨百翰大学）；Masdar Institute of Science and Technology（马斯达尔科学与技术学院）；British University in Dubai（迪拜英国大学）；University of Edinburgh（爱丁堡大学）；Toulouse School of Economics（图卢兹经济学院）；Center for Research in Management（管理研究中心）；Centre National de la Recherche Scientifique（法国国家科学研究中心）；Institute for Advanced Study in Toulouse（图卢兹高等研究院）；University of Toulouse Capitole（图卢兹第一大学）；Data61（数据61）；Commonwealth Scientific and Industrial Research Organization（澳大利亚联邦科学与工业研究组织）；University of California, Irvine（加州大学欧文分校）；The Media Lab（媒体实验室）；Massachusetts Institute of Technology（麻省理工学院）
Jacob W. Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A. Goodrich, Iyad Rahwan
Since Alan Turing envisioned Artificial Intelligence (AI) [1], a major driving force behind technical progress has been competition with human cognition. Historical milestones have been frequently associated with computers matching or outperforming humans in difficult cognitive tasks (e.g. face recognition [2], personality classification [3], driving cars [4], or playing video games [5]), or defeating humans in strategic zero-sum encounters (e.g. Chess [6], Checkers [7], Jeopardy! [8], Poker [9], or Go [10]). In contrast, less attention has been given to developing autonomous machines that establish mutually cooperative relationships with people who may not share the machine's preferences. A main challenge has been that human cooperation does not require sheer computational power, but rather relies on intuition [11], cultural norms [12], emotions and signals [13, 14, 15, 16], and pre-evolved dispositions toward cooperation [17], common-sense mechanisms that are difficult to encode in machines for arbitrary contexts. Here, we combine a state-of-the-art machine-learning algorithm with novel mechanisms for generating and acting on signals to produce a new learning algorithm that cooperates with people and other machines at levels that rival human cooperation in a variety of two-player repeated stochastic games. This is the first general-purpose algorithm that is capable, given a description of a previously unseen game environment, of learning to cooperate with people within short timescales in scenarios previously unanticipated by algorithm designers. This is achieved without complex opponent modeling or higher-order theories of mind, thus showing that flexible, fast, and general human-machine cooperation is computationally achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.
Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas
Northwestern Polytechnical University（西北工业大学）；Tsinghua University（清华大学）；University of Auckland（奥克兰大学）；Kyushu University（九州大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
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）
Cooperation Through Indirect Reciprocity in Child-Robot Interactions
Lasige, Faculdade de Ciências, Universidade de Lisboa（里斯本大学科学学院）；Institute of Informatics, University of Amsterdam（阿姆斯特丹大学信息学院）；Interactive Technologies Institute, Instituto Superior Técnico, Universidade de Lisboa（里斯本技术学院互动技术研究所）
The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?
University College London（伦敦大学学院）；Middlesex University（密德萨斯大学）
An Extension of BIM Using AI: a Multi Working-Machines Pathfinding Solution
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University of California at Berkeley（加州大学伯克利分校）；Peking University（北京大学）；University of Technology Sydney（悉尼科技大学）
Overcoming the Machine Penalty with Imperfectly Fair AI Agents
School of Cybersecurity, and School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, China（网络安全学院和人工智能学院，光学电子研究所（iOPEN），西北工业大学，中国）；Faculty of Engineering Sciences, Kyushu University, Japan（工程科学学院，九州大学，日本）；School of Computing, Engineering and Digital Technologies, Teesside University, United Kingdom（计算、工程与数字技术学院，泰赛德大学，英国）；Computer Science, Science Division, New York University Abu Dhabi, UAE（计算机科学，科学系，纽约大学阿布扎比分校，阿联酋）；School of Statistics and Mathematics, Yunnan University of Finance and Economics, China（统计与数学学院，云南财经大学，中国）；Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室，中国）
