Heuristic Search for Bounded Model Checking of Probabilistic Automata

We describe a new method for bounded model-checking of probabilistic automata (PAs), based on heuristic search and integrated into the PRISM model-checker. PA models include both probabilistic transitions and nondeterministic choice transitions. Our search-based approach aims to address weaknesses in statistical and dynamic-programming approaches to checking bounded PCTL properties of probabilistic automata. To model-check properties of PA models, we must demonstrate that the property will be satisfied even in the face of an adversary that optimally resolves nondeterministic choices. We use heuristically guided AO* search to find the optimal adversary policy and estimate the probability of properties. We adapt techniques from Artificial Intelligence Planning to develop a heuristic that is based on a relaxation of the underlying probability model. This heuristic provides critical guidance to the search algorithm: without it, even very small models are unsolvable. We have implemented our algorithm in the PRISM model-checker, and show cases where it outperforms PRISM’s dynamic programming methods. We also describe promising directions for future work in search-based PA model-checking, notably introducing further abstraction into the heuristic to address memory issues.