Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction
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DOI: 10.1109/lcsys.2022.3173993
Abstract: We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Logic (scLTL). We propose an abstraction-based approach that iteratively generates high-confidence Interval Markov Decision… read more here.
Keywords: learning uncertainty; planning via; uncertainty aware; aware planning ... See more keywords