REVECA：基于信息相关性与相对邻近性的协作语言代理自适应规划与轨迹验证
REVECA: Adaptive Planning and Trajectory-based Validation in Cooperative Language Agents using Information Relevance and Relative Proximity
AI总结：
针对多智能体协作中信息处理低效、全局次优及环境变化引发错误规划的挑战，提出由GPT-4o-mini驱动的REVECA认知架构，通过相关性估计、自适应规划和轨迹验证实现高效记忆与最优规划，在多基准上表现优异并具可信人机协作潜力。
摘要：
我们应对多智能体协作的挑战，其中智能体在复杂部分观测下与去中心化智能体合作以实现共同目标。现有协作智能体系统常难以高效处理持续累积的信息，因缺乏对协作者的考量而导致全局次优规划，且难以解决由其他协作者引入的环境变化所引发的错误规划。为克服这些挑战，我们提出了REVECA（相关性、邻近性与验证增强的协作语言代理），一种由GPT-4o-mini驱动的新型认知架构。REVECA通过利用相关性估计、自适应规划和基于轨迹的验证，实现了高效的记忆管理、最优规划以及高性价比的错误规划预防。大量实验结果证明REVECA在多种基准上优于现有方法，同时用户研究揭示了其在实现可信人机协作方面的潜力。
英文摘要：
We address the challenge of multi-agent cooperation, where agents achieve a common goal by cooperating with decentralized agents under complex partial observations. Existing cooperative agent systems often struggle with efficiently processing continuously accumulating information, managing globally suboptimal planning due to lack of consideration of collaborators, and addressing false planning caused by environmental changes introduced by other collaborators. To overcome these challenges, we propose the RElevance, Proximity, and Validation-Enhanced Cooperative Language Agent (REVECA), a novel cognitive architecture powered by GPT-4o-mini. REVECA enables efficient memory management, optimal planning, and cost-effective prevention of false planning by leveraging Relevance Estimation, Adaptive Planning, and Trajectory-based Validation. Extensive experimental results demonstrate REVECA's superiority over existing methods across various benchmarks, while a user study reveals its potential for achieving trustworthy human-AI cooperation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
