Belief-Aware Multi-Agent Path Finding under Map Uncertainty
[Sztuczna inteligencja] Autorzy: Viraj Parimi, Shao-Hung Chan, Han Zhang i 2 więcej
Multi-Agent Path Finding (MAPF) aims to find collision-free paths for multiple agents in a shared environment. Classical MAPF assumes that all static obstacles are known in advance, but real-world environments can change unexpectedly due to fallen objects, spills, or other local disturbances. When such changes are spatially correlated, an observation can inform traversability estimates beyond the observed location. Prior approaches address uncertainty in traversability through contingent plans or replanning based on direct observations, but do not leverage this spatial dependence to infer the traversability of nearby unobserved locations. As a result, they cannot use one observation to anticipate nearby unobserved obstacles that may cause costly rerouting later. We focus on Belief-Aware MAPF, where map discrepancies are fixed during execution but initially unknown, and observations can …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja