arXiv:2603.23405v2 Announce Type: replace-cross Abstract: Modern Multi-Agent Path Finding (MAPF) algorithms must plan for hundreds to thousands of agents in congested environments within a second, requiring highly efficient algorithms. Priority Inheritance with Backtracking (PIBT) is a popular algorithm capable of effectively planning in such situations. However, PIBT, and its variants like Enhanced PIBT (EPIBT), is constrained by its rule-based planning procedure and lacks generality because it restricts its search to paths that collide with at most one other agent. In this paper, we describe
The paper addresses current limitations in highly efficient multi-agent pathfinding, crucial for the increasing complexity and scale of multi-agent systems being developed.
Improved MAPF algorithms enable more sophisticated and robust coordination for large teams of autonomous agents, directly impacting the feasibility and efficiency of AI agents and robotics.
The proposed 'Multi-Dependency PIBT' generalizes existing algorithms, allowing for more complex multi-agent interactions and potentially unlocking new capabilities for AI coordination.
- · AI algorithm developers
- · Robotics industry
- · Logistics and automation companies
- · Autonomous systems integrators
- · Companies reliant on less efficient, rule-based MAPF
- · Industries with simple automation needs
Enhances the ability of AI systems to manage complex, interactive tasks involving numerous agents.
Accelerates the development and deployment of commercial humanoid robots and other multi-robot systems in cluttered or dynamic environments.
Could enable more complex and adaptable supply chain automation and urban mobility solutions, reducing operational costs and increasing resilience.
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Read at arXiv cs.AI