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Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models

Saaduddin Mahmud, Dorian Benhamou Goldfajn, Shlomo Zilberstein

TL;DR

This work introduces VL-DCOPs, a multimodal extension of distributed constraint optimization that leverages large multimodal foundation models to auto-create constraints from visual and linguistic inputs. It defines three agent archetypes—A1 (FMC-DSA), A2 (CoPA), and A3 (MDP-based algorithm simulator)—and evaluates them on three benchmarks (LDGC, VLDGC, LDMS) using a range of LLMs and VLMs to reveal trade-offs between efficiency, scalability, and robustness. The study highlights that LFMs can address distributed coordination tasks off-the-shelf, with A1 offering solid immediate performance, A2 achieving cost-consensus through interaction, and A3 providing a flexible, though costly, general coordinator capable of simulating various algorithms. The findings underscore the potential for edge deployment and real-time coordination, while outlining future directions in adaptive coordination, interpretability, and privacy to ensure practical, secure deployment in dynamic environments.

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.

Distributed Multi-Agent Coordination Using Multi-Modal Foundation Models

TL;DR

This work introduces VL-DCOPs, a multimodal extension of distributed constraint optimization that leverages large multimodal foundation models to auto-create constraints from visual and linguistic inputs. It defines three agent archetypes—A1 (FMC-DSA), A2 (CoPA), and A3 (MDP-based algorithm simulator)—and evaluates them on three benchmarks (LDGC, VLDGC, LDMS) using a range of LLMs and VLMs to reveal trade-offs between efficiency, scalability, and robustness. The study highlights that LFMs can address distributed coordination tasks off-the-shelf, with A1 offering solid immediate performance, A2 achieving cost-consensus through interaction, and A3 providing a flexible, though costly, general coordinator capable of simulating various algorithms. The findings underscore the potential for edge deployment and real-time coordination, while outlining future directions in adaptive coordination, interpretability, and privacy to ensure practical, secure deployment in dynamic environments.

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.
Paper Structure (25 sections, 1 equation, 1 figure, 4 tables, 4 algorithms)

This paper contains 25 sections, 1 equation, 1 figure, 4 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. DCOPs Framework
  4. DCOPs Algorithm
  5. LFM Centerd Decision Making
  6. LFM Aided Constraint Programming
  7. LFM Centered Agent Modeling
  8. Background
  9. Details of VL-DCOPs
  10. Agents for VL-DCOPs
  11. A1: Instruction Following DCOP Agent
  12. A2: Conversational DCOP Agent
  13. A3: Algorithm Simulating DCOP Agent
  14. MDP Definition:
  15. Simulating FMC-DSA with A3
  16. ...and 10 more sections

Figures (1)

  • Figure 1: Spectrum of VL-DCOP Agents solving language-conditioned weighted graph coloring problems.