arXiv:2601.04884v3 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 wh
The increasing complexity and autonomy of multi-agent systems, particularly in AI, necessitate more sophisticated and efficient replanning capabilities to ensure reliable operation.
This research provides a method to significantly enhance the resilience and efficiency of multi-agent systems, which is critical for the deployment of advanced AI agents in real-world scenarios.
The ability to precompute and utilize temporal flexibility allows for much quicker and more effective replanning in dynamic multi-agent environments, reducing disruption and improving system performance.
- · AI agents developers
- · Logistics and robotics companies
- · Autonomous system operators
- · Systems relying on inefficient replanning
- · Manual oversight in complex robotic operations
More robust and efficient multi-agent systems become viable across various applications.
Accelerated development and adoption of autonomous AI systems due to improved reliability and safety.
Increased integration of AI agents into critical infrastructure and complex operational environments.
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Read at arXiv cs.AI