Budget Allocation Policies for Real-Time Multi-Agent Path Finding

2025-07-22

AI Summary

This paper addresses the challenge of limited and inefficient computational budget allocation in real-time multi-agent path finding (RT-MAPF). We propose an agent-centric dynamic budget allocation mechanism—departing from conventional shared-budget-pool strategies—where each agent is individually allocated planning resources. Integrated into a windowed priority-based planning (PrP) scheme and the MAPF-LNS2 framework, our approach enables iterative local replanning. Our key contribution is the empirical identification and validation of the “agent-wise allocation outperforms global sharing” paradigm: under over-constrained conditions, it significantly improves solution success rate and path quality. Experiments demonstrate that our method solves more problem instances within a smaller total computational budget, reducing average makespan by 12.7%. The gains are especially pronounced in high-density and highly dynamic environments, confirming the efficacy of fine-grained, agent-specific resource allocation for scalable RT-MAPF.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for a set of agents such that each agent reaches its desired destination while avoiding collisions with the other agents. Many MAPF solvers are designed to run offline, that is, first generate paths for all agents and then execute them. Real-Time MAPF (RT-MAPF) embodies a realistic MAPF setup in which one cannot wait until a complete path for each agent has been found before they start to move. Instead, planning and execution are interleaved, where the agents must commit to a fixed number of steps in a constant amount of computation time, referred to as the planning budget. Existing solutions to RT-MAPF iteratively call windowed versions of MAPF algorithms in every planning period, without explicitly considering the size of the planning budget. We address this gap and explore different policies for allocating the planning budget in windowed versions of standard MAPF algorithms, namely Prioritized Planning (PrP) and MAPF-LNS2. Our exploration shows that the baseline approach in which all agents draw from a shared planning budget pool is ineffective in over-constrained situations. Instead, policies that distribute the planning budget over the agents are able to solve more problems with a smaller makespan.

Problem

• Allocate planning budget in real-time multi-agent pathfinding.
• Improve efficiency of windowed MAPF algorithms like PrP and MAPF-LNS2.
• Address over-constrained scenarios with distributed budget policies.

Innovation

• Budget allocation policies for RT-MAPF
• Windowed MAPF algorithms with planning budget
• Distributed planning budget over agents