Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models

• URL: http://arxiv.org/abs/2501.14189v1
• Date: Fri, 24 Jan 2025 02:50:21 GMT
• Title: Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models
• Authors: Saaduddin Mahmud, Dorian Benhamou Goldfajn, Shlomo Zilberstein,
• Abstract summary: Distributed Constraint Optimization Problems (DCOPs) offer a powerful framework for multi-agent coordination but often rely on labor-intensive, manual problem construction.<n>We introduce a framework that takes advantage of large multimodal foundation models (LFMs) to automatically generate constraints from both visual and linguistic instructions.<n>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.
• Score: 9.37268652939886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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