Diplomacy is one of the most sophisticated activities in human society. The complex interactions among multiple parties/ agents involve various abilities like social reasoning, negotiation arts, and long-term strategy planning. Previous AI agents surely have proved their capability of handling multi-step games and larger action spaces on tasks involving multiple agents. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. Recently, LLM agents have shown their potential for extending the boundary of previous agents on a couple of applications, however, it is still not enough to handle a very long planning period in a complex multi-agent environment. Empowered with cutting-edge LLM technology, we make the first stab to explore AI's upper bound towards a human-like agent for such a highly comprehensive multi-agent mission by combining three core and essential capabilities for stronger LLM-based societal agents: 1) strategic planner with memory and reflection; 2) goal-oriented negotiate with social reasoning; 3) augmenting memory by self-play games to self-evolving without any human in the loop.

翻译：外交是人类社会最复杂的活动之一。多方/多智能体之间的复杂交互涉及社会推理、谈判艺术与长期战略规划等多种能力。先前的人工智能体已在多智能体任务中证明了其处理多步博弈与大规模动作空间的能力。然而，外交活动涉及极其庞大的决策空间，尤其在考虑必要谈判阶段时更为显著。近期，基于大型语言模型的智能体已在若干应用中展现出突破传统智能体边界的潜力，但仍不足以应对复杂多智能体环境中超长周期的规划任务。借助前沿大型语言模型技术，我们首次尝试通过整合三大核心能力来探索人工智能在此类高度复杂的多智能体任务中拟人化智能体的性能上限，以构建更强大的基于大型语言模型的社会性智能体：1）具备记忆与反思能力的战略规划器；2）基于社会推理的目标导向型谈判模块；3）通过自我博弈增强记忆以实现完全自主的持续演化机制。