We address the challenge of multi-agent cooperation, where agents achieve a
common goal by interacting with a 3D scene and cooperating with decentralized
agents under complex partial observations. This involves managing communication
costs and optimizing interaction trajectories in dynamic environments. Our
research focuses on three primary limitations of existing cooperative agent
systems. Firstly, current systems demonstrate inefficiency in managing acquired
information through observation, resulting in declining planning performance as
the environment becomes more complex with additional objects or goals.
Secondly, the neglect of false plans in partially observable settings leads to
suboptimal cooperative performance, as agents struggle to adapt to
environmental changes influenced by the unseen actions of other agents. Lastly,
the failure to incorporate spatial data into decision-making processes
restricts the agent's ability to construct optimized trajectories. To overcome
these limitations, we propose the RElevance and Validation-Enhanced Cooperative
Language Agent (REVECA), a novel cognitive architecture powered by GPT-3.5.
REVECA leverages relevance assessment, plan validation, and spatial information
to enhance the efficiency and robustness of agent cooperation in dynamic and
partially observable environments while minimizing continuous communication
costs and effectively managing irrelevant dummy objects. Our extensive
experiments demonstrate the superiority of REVECA over previous approaches,
including those driven by GPT-4.0. Additionally, a user study highlights
REVECA's potential for achieving trustworthy human-AI cooperation. We expect
that REVECA will have significant applications in gaming, XR applications,
educational tools, and humanoid robots, contributing to substantial economic,
commercial, and academic advancements.

我们通过与三维场景进行互动和协作来解决多智能体合作的挑战，重点解决现有合作智能体系统的三个主要限制，并提出 REVECA，这是一种由 GPT-3.5 驱动的新型认知架构，利用相关性评估、计划验证和空间信息来增强智能体在动态和部分可观测环境中的合作效率和鲁棒性。

基于 LLM 的合作智能体，利用信息相关性和计划验证

LLM-Based Cooperative Agents using Information Relevance and Plan  Validation

Robust coordination skills enable agents to operate cohesively in shared
environments, together towards a common goal and, ideally, individually without
hindering each other's progress. To this end, this paper presents Coordinated
QMIX (CoMIX), a novel training framework for decentralized agents that enables
emergent coordination through flexible policies, allowing at the same time
independent decision-making at individual level. CoMIX models selfish and
collaborative behavior as incremental steps in each agent's decision process.
This allows agents to dynamically adapt their behavior to different situations
balancing independence and collaboration. Experiments using a variety of
simulation environments demonstrate that CoMIX outperforms baselines on
collaborative tasks. The results validate our incremental policy approach as
effective technique for improving coordination in multi-agent systems.

Coordinated QMIX (CoMIX) is a training framework for decentralized agents that enables emergent coordination through flexible policies, improving coordination in multi-agent systems.