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arXiv · 2507.22282

Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees

Kegan J. Strawn·Thomy Phan·Eric Wang·Nora Ayanian·Sven Koenig·Lars Lindemann
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Abstract

Existing multi-agent path finding (MAPF) solvers do not account for uncertain behavior of uncontrollable agents. We present a novel variant of Enhanced Conflict-Based Search (ECBS), for both one-shot and lifelong MAPF in dynamic environments with uncontrollable agents. Our method consists of (1) training a learned predictor for the movement of uncontrollable agents, (2) quantifying the prediction error using conformal prediction (CP), a tool for statistical uncertainty quantification, and (3) integrating these uncertainty intervals into our modified ECBS solver. Our method can account for uncertain agent behavior, comes with statistical guarantees on collision-free paths for one-shot missions, and scales to lifelong missions with a receding horizon sequence of one-shot instances. We run our algorithm, CP-Solver, across warehouse and game maps, with competitive throughput and reduced collisions.

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Kegan J. Strawn, Thomy Phan, Eric Wang, Nora Ayanian, Sven Koenig, Lars Lindemann. 2025-07-29. Multi-Agent Path Finding Among Dynamic Uncontrollable Agents with Statistical Safety Guarantees. https://arxiv.org/abs/2507.22282

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