Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917155199516672 |
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| author | Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou |
| author_facet | Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_06813 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou Artificial Intelligence Multiagent Systems Social and Information Networks 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. |
| title | Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy |
| topic | Artificial Intelligence Multiagent Systems Social and Information Networks |
| url | https://arxiv.org/abs/2407.06813 |