用算法自动生成多样人类协作行为，提升人机团队研究效率。
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models
- 结合质量多样性优化与大模型生成提示词，自动搜索多样化协作策略。
- 在多步协作环境中生成了人类难以观察的复杂行为模式。
- 生成行为经实验证明具人类相似性，适合研究人机协作机制。
理解人类在团队中的协作与沟通方式对提升人机协同和人工智能辅助决策至关重要。然而，依赖大规模用户研究获取数据存在后勤、伦理和实际限制，需依赖合成的人类行为模型。近期研究表明，基于大语言模型（LLMs）的智能体可在社交场景中模拟人类行为。但生成大量多样化行为仍需手动设计提示词。质量多样性（QD）优化已被证明可生成多样化的强化学习（RL）智能体行为。本文将QD优化与LLM驱动的智能体结合，在长期、多步骤协作环境中迭代搜索能生成多样化团队行为的提示词。首先通过人类实验表明，人类在此领域表现出多样化的协调与沟通行为。随后一系列实验显示，该方法捕捉到传统数据收集难以观测的行为，后续用户研究也证实生成行为具有人类相似性。结果表明，QD与LLM智能体结合是研究多智能体协作策略的有效工具。
原文摘要 · Abstract (English)
Understanding how humans collaborate and communicate in teams is essential for improving human-agent teaming and AI-assisted decision-making. However, relying solely on data from large-scale user studies is impractical due to logistical, ethical, and practical constraints, necessitating synthetic models of multiple diverse human behaviors. Recently, agents powered by Large Language Models (LLMs) have been shown to emulate human-like behavior in social settings. But, obtaining a large set of diverse behaviors requires manual effort in the form of designing prompts. On the other hand, Quality Diversity (QD) optimization has been shown to be capable of generating diverse Reinforcement Learning (RL) agent behavior. In this work, we combine QD optimization with LLM-powered agents to iteratively search for prompts that generate diverse team behavior in a long-horizon, multi-step collaborative environment. We first show, through a human-subjects experiment, that humans exhibit diverse coordination and communication behavior in this domain. We then present a series of experiments showing that our approach captures behaviors that are difficult to observe without large-scale data collection, and a follow-up user study to show that these generated behaviors are human-like. Our findings highlight the combination of QD and LLM-powered agents as an effective tool for studying teaming and communication strategies in multi-agent collaboration.
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