New Algorithm Efficiently Replans Multi-Agent Paths When Delays Occur
Researchers have developed FlexSIPP, an algorithm that efficiently replans the path of a single delayed agent in a multi-agent system by leveraging the temporal flexibility of other agents. The work addresses a core challenge in multi-agent path finding (MAPF): when one agent is delayed, replanning only that agent can be infeasible, while replanning all agents is computationally expensive and risks cascading delays. The algorithm was validated on the Dutch railway network and a standard MAPF benchmark, suggesting practical applicability to real-world scheduling systems.
FlexSIPP is a new algorithm for multi-agent path replanning that targets the specific scenario where a single agent experiences a delay, a common and disruptive event in coordinated systems. The key innovation is the concept of 'temporal flexibility,' defined as the maximum delay any given agent can absorb without altering its ordering relative to other agents or propagating further delays. By precomputing all feasible plans for the delayed agent and tracking this flexibility across the remaining agents, FlexSIPP avoids the cascade of changes that typically makes full replanning expensive. The algorithm was accepted at the Symposium on Combinatorial Search (SoCS'26) and tested on two distinct environments: the densely-used Dutch national railway network and the MovingAI MAPF benchmark suite. Experiments indicate that FlexSIPP produces effective, real-world-relevant solutions within practical time constraints. The research represents a targeted middle ground between the two extremes of replanning only the delayed agent versus replanning all agents simultaneously.
What's missing
The paper does not specify the computational complexity bounds of FlexSIPP relative to existing MAPF replanning algorithms, nor does it discuss how performance degrades as the number of agents or network density scales beyond the tested benchmarks. It is also unclear how the algorithm handles simultaneous delays affecting multiple agents at once.
What different sources said
- arXiv cs.AICenter
Precomputing Multi-Agent Path Replanning Using Temporal Flexibility
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