Efficient Multi-Agent Coordination via Dynamic Joint-State Graph Construction

2025-09-08

AI Summary

This work addresses the lack of proactive collaboration in Multi-Agent Path Finding (MAPF) by proposing a novel paradigm—Team Coordination on Risky-Edge Graphs (TCGRE)—which mandates agents to dynamically coordinate to reduce traversal costs on high-risk edges. To solve TCGRE efficiently, we introduce the Homogeneous Joint State Graph (HJSG), leveraging agent homogeneity to reduce joint state-space complexity from exponential to polynomial while preserving optimality. Our method integrates 3D matching modeling, Coordination Exhaustive Search (CES), and Rolling-Horizon Optimized Conflict-Based A* (RHOCA*), enabling real-time coordination in large-scale scenarios. Experiments demonstrate that HJSG significantly outperforms baseline approaches across diverse graph topologies, achieving 10×–100× speedup in computation time and exhibiting strong scalability.

Technology Category

• Multiagent Systems: Teamwork
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

Multi-agent pathfinding (MAPF) traditionally focuses on collision avoidance, but many real-world applications require active coordination between agents to improve team performance. This paper introduces Team Coordination on Graphs with Risky Edges (TCGRE), where agents collaborate to reduce traversal costs on high-risk edges via support from teammates. We reformulate TCGRE as a 3D matching problem-mapping robot pairs, support pairs, and time steps-and rigorously prove its NP-hardness via reduction from Minimum 3D Matching. To address this complexity, (in the conference version) we proposed efficient decomposition methods, reducing the problem to tractable subproblems: Joint-State Graph (JSG): Encodes coordination as a single-agent shortest-path problem. Coordination-Exhaustive Search (CES): Optimizes support assignments via exhaustive pairing. Receding-Horizon Optimistic Cooperative A* (RHOCA*): Balances optimality and scalability via horizon-limited planning. Further in this extension, we introduce a dynamic graph construction method (Dynamic-HJSG), leveraging agent homogeneity to prune redundant states and reduce computational overhead by constructing the joint-state graph dynamically. Theoretical analysis shows Dynamic-HJSG preserves optimality while lowering complexity from exponential to polynomial in key cases. Empirical results validate scalability for large teams and graphs, with HJSG outperforming baselines greatly in runtime in different sizes and types of graphs. This work bridges combinatorial optimization and multi-agent planning, offering a principled framework for collaborative pathfinding with provable guarantees, and the key idea of the solution can be widely extended to many other collaborative optimization problems, such as MAPF.

Problem

• Active coordination among agents to improve team performance
• Collaborative traversal cost reduction on high-risk edges via teammate support
• Solving NP-hard 3D matching problem for multi-agent pathfinding optimization

Innovation

• Dynamic joint-state graph construction for coordination
• Receding-horizon planning balancing optimality and scalability
• Homogeneity-based state pruning to reduce computation