用多模态大模型自动生成多智能体协作约束，提升自动化水平。
Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models
- 利用视觉与语言指令自动构建多智能体约束
- 提出从神经符号到全神经的智能体范式谱系
- 在三个新任务上验证了不同范式的优劣
分布式约束优化问题（DCOPs）为多智能体协作提供了强大框架，但通常依赖繁琐的手动建模。为此，我们提出VL-DCOPs框架，利用大规模多模态基础模型（LFMs）从视觉和语言指令中自动生成约束。我们设计了一组智能体原型：从将部分算法决策交给LFM的神经符号型，到完全依赖LFM进行协调的全神经型。我们在三个新型VL-DCOP任务上，使用最先进的大语言模型（LLMs）和视觉语言模型（VLMs）评估这些智能体原型，并比较其各自优势与局限。最后，探讨该工作对DCOP文献中更广泛前沿挑战的延伸意义。
原文摘要 · Abstract (English)
Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction. To address this, we introduce VL-DCOPs, a framework that takes advantage of large multimodal foundation models (LFMs) to automatically generate constraints from both visual and linguistic instructions. We then introduce a spectrum of agent archetypes for solving VL-DCOPs: from a neuro-symbolic agent that delegates some of the algorithmic decisions to an LFM, to a fully neural agent that depends entirely on an LFM for coordination. We evaluate these agent archetypes using state-of-the-art LLMs (large language models) and VLMs (vision language models) on three novel VL-DCOP tasks and compare their respective advantages and drawbacks. Lastly, we discuss how this work extends to broader frontier challenges in the DCOP literature.
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