An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.
Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
East China Normal University（华东师范大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Bytedance Inc（字节跳动）；Canadian Collaborative Entrepreneurs Organization Inc（加拿大合作企业家组织）
The Power of Stories: Narrative Priming Shapes How LLM Agents Collaborate and Compete
Department of Data Science and its Applications, German Research Center for Artificial Intelligence (DFKI)（数据科学及其应用系，德国人工智能研究中心（DFKI））；Department of Computer Science, University of Kaiserslautern–Landau (RPTU)（计算机科学系，凯撒斯劳滕-兰道大学（RPTU））；Department of Language Science and Technology, Saarland University（语言科学与技术系，萨尔兰大学）
本文通过有限重复公共物品博弈实验发现，基于故事的叙事启动能显著影响 LLM 智能体的合作与竞争策略，共同叙事促进合作，而不同叙事使自利倾向智能体占优，对多智能体系统设计和 AI 对齐具有启示。
