When Should Agents Coordinate in Differentiable Sequential Decision Problems?

2026-02-03

AI Summary

This work addresses the trade-off in multi-agent systems between performance degradation under uncoordinated execution and the high communication overhead of explicit coordination. The authors propose a novel on-demand coordination mechanism that conceptualizes coordination as a continuum ranging from joint optimization to Nash equilibrium. Crucially, they establish the first connection between coordination decisions and second-order properties of the objective function—specifically, the structure of the Hessian—by dynamically analyzing its curvature to determine when coordination is necessary. Integrating differentiable motion planning with game-theoretic principles, the proposed algorithm accurately identifies critical moments requiring coordination, thereby maintaining high task performance while substantially reducing communication costs.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Multi-robot teams must coordinate to operate effectively. When a team operates in an uncoordinated manner, and agents choose actions that are only individually optimal, the team's outcome can suffer. However, in many domains, coordination requires costly communication. We explore the value of coordination in a broad class of differentiable motion-planning problems. In particular, we model coordinated behavior as a spectrum: at one extreme, agents jointly optimize a common team objective, and at the other, agents make unilaterally optimal decisions given their individual decision variables, i.e., they operate at Nash equilibria. We then demonstrate that reasoning about coordination in differentiable motion-planning problems reduces to reasoning about the second-order properties of agents'objectives, and we provide algorithms that use this second-order reasoning to determine at which times a team of agents should coordinate.

Problem

• coordination
• multi-agent systems
• differentiable motion planning
• Nash equilibrium
• sequential decision making

Innovation

• differentiable coordination
• second-order reasoning
• multi-agent motion planning
• Nash equilibrium
• communication efficiency