Progression Heuristics for Planning with Probabilistic LTL Constraints

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 problems.

Automated summary

This study introduces novel admissible heuristics to guide the search for cost-optimal policies under multi-objective probabilistic temporal logic constraints. These heuristics estimate the probability that a partial policy extension satisfies the constraints by projecting and decomposing LTL formulae obtained through progression. Experimental results show that these heuristics further enhance the scalability gap between heuristic search and verification approaches to these planning problems.