When Agents Break Down in Multiagent Path Finding

2025-08-05

AI Summary

To address delays caused by sudden agent failures in multi-agent path finding (MAPF), this paper proposes a dynamic scheduling adaptation framework that avoids global replanning. The method introduces a distributed, fault-resilient coordination mechanism comprising two novel communication protocols: one guarantees that the makespan increase is strictly bounded by *k* timesteps (*k* being the number of failed agents), while the other offloads computational load to networked infrastructure nodes to reduce on-agent overhead. It further integrates localized conflict resolution with online path adjustment and provides theoretical bounds on solution quality and runtime complexity. Experiments demonstrate that the approach significantly reduces replanning cost, maintains near-optimal scheduling efficiency, and scales effectively with agent count—showcasing strong practical deployability for real-world MAPF systems.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

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

Abstract

In Multiagent Path Finding (MAPF), the goal is to compute efficient, collision-free paths for multiple agents navigating a network from their sources to targets, minimizing the schedule's makespan-the total time until all agents reach their destinations. We introduce a new variant that formally models scenarios where some agents may experience delays due to malfunctions, posing significant challenges for maintaining optimal schedules. Recomputing an entirely new schedule from scratch after each malfunction is often computationally infeasible. To address this, we propose a framework for dynamic schedule adaptation that does not rely on full replanning. Instead, we develop protocols enabling agents to locally coordinate and adjust their paths on the fly. We prove that following our primary communication protocol, the increase in makespan after k malfunctions is bounded by k additional turns, effectively limiting the impact of malfunctions on overall efficiency. Moreover, recognizing that agents may have limited computational capabilities, we also present a secondary protocol that shifts the necessary computations onto the network's nodes, ensuring robustness without requiring enhanced agent processing power. Our results demonstrate that these protocols provide a practical, scalable approach to resilient multiagent navigation in the face of agent failures.

Problem

• Handling agent malfunctions in Multiagent Path Finding efficiently
• Avoiding full replanning by dynamic schedule adaptation
• Ensuring robustness with limited agent computational capabilities

Innovation

• Dynamic schedule adaptation without full replanning
• Local coordination to adjust paths on the fly
• Shift computations to network nodes for robustness