Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI's potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection; 2) goal-oriented negotiation with social reasoning; and 3) augmenting memory through self-play games for self-evolution without human in the loop.

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI's potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection; 2) goal-oriented negotiation with social reasoning; and 3) augmenting memory through self-play games for self-evolution without human in the loop.

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.