Progression Heuristics for Planning with Probabilistic LTL Constraints
Mallet, I., Thiébaux, S. and Trevizan, F.
To appear in Proc. of 35th AAAI Conference on Artificial Intelligence.
We are working on the camera-ready of this paper and it will be available soon.
Bellow is the abstract of this paper.
Probabilistic planning subject to multi-objective probabilistic temporal
logic (PLTL) constraints models the problem of computing safe and robust
behaviours for agents in stochastic environments. We present novel
admissible heuristics to guide the search for cost-optimal policies for
these problems. These heuristics project and decompose LTL formulae
obtained by progression to estimate the probability that an extension of a
partial policy satisfies the constraints. Their computation with linear
programming is integrated with the recent PLTL-dual heuristic search
algorithm, enabling more aggressive pruning of regions violating the
constraints. Our experiments show that they further widen the scalability
gap between heuristic search and verification approaches to these planning