aiqbench analysis
This research introduces FlexSIPP, an algorithm that efficiently handles multi-agent path planning disruptions by precomputing temporal flexibility to avoid cascading delays in dense networks like railways. By optimizing replanning for a single delayed agent without triggering widespread changes, the method offers a practical solution for real-time adjustments in complex logistics and transportation systems.
Precomputing Multi-Agent Path Replanning Using Temporal Flexibility
arXiv:2601.04884v4 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not yield an efficient plan, and sometimes cannot even yield a feasible one. On the other hand, replanning other agents may lead to a cascade of changes and delays, and it is computationally expensive. We show how to efficiently replan a single delayed agent by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay that the agent can take without changing the order with agents other than the initially delayed agent, or further delaying other agents. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent and returns the changes to the other agents within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network and in the MovingAI MAPF benchmark set. Our experiments show that FlexSIPP provides effective solutions relevant to real-world adjustments, and within a reasonable timeframe.
Abstract:Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to quickly find a new safe plan. Replanning only the delayed agent often does not yield an efficient plan, and sometimes cannot even yield a feasible one. On the other hand, replanning other agents may lead to a cascade of changes and delays, and it is computationally expensive. We show how to efficiently replan a single delayed agent by tracking and using the temporal flexibility of other agents while avoiding cascading delays. This flexibility is the maximum delay that the agent can take without changing the order with agents other than the initially delayed agent, or further delaying other agents. Our algorithm, FlexSIPP, precomputes all possible plans for the delayed agent and returns the changes to the other agents within the given scenario. We demonstrate our method in a real-world case study of replanning trains in the densely-used Dutch railway network and in the MovingAI MAPF benchmark set. Our experiments show that FlexSIPP provides effective solutions relevant to real-world adjustments, and within a reasonable timeframe.
Revision after a bug was found in the code. The fixes do not alter the conclusions; the MAPF results are slightly different from those published at SoCS26. The railway results changed as the code was rearranged, now returning proper valid paths, with a new data representation to show the actual differences between FlexSIPP and MAEDeR. In the Fig1 example a3s route changed to show a1s rerouting
Artificial Intelligence (cs.AI)
arXiv:2601.04884 [cs.AI]
(or arXiv:2601.04884v4 [cs.AI] for this version)
https://doi.org/10.1609/socs.v19i1.43072 DOI(s) linking to related resources
Full text under the publisher's open licence · 271 words
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