Summary of Instigating Cooperation Among Llm Agents Using Adaptive Information Modulation, by Qiliang Chen et al.
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation
by Qiliang Chen, Sepehr Ilami, Nunzio Lore, Babak Heydari
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel framework that combines Large Language Model (LLM) agents with reinforcement learning (RL) to simulate strategic interactions within team environments. The LLM agents serve as proxies for human behavior, while the RL agent modulates information access across the network to optimize social welfare and promote pro-social behavior. The authors validate their approach in iterative games, including the prisoner’s dilemma, and demonstrate that the LLM agents exhibit nuanced strategic adaptations. The RL agent learns to adjust information transparency, leading to enhanced cooperation rates. This framework provides insights into AI-mediated social dynamics and has implications for deploying AI in real-world team settings. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence (AI) can help people work together better. It uses special computer programs called Large Language Models to simulate human behavior, and then adds a new layer of rules that helps the programs make good decisions. The goal is to promote cooperation and fairness in groups. The authors tested their idea with some simple games and found that it works well. This research can help us understand how AI can be used to improve teamwork and collaboration. |
Keywords
» Artificial intelligence » Large language model » Reinforcement learning