Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes

Abstract

Probabilistic models are widely deployed in various systems. To ensure their correctness, verification techniques have been developed to analyze probabilistic systems. We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process. Different from previous works, weighted assumptions are introduced to attain completeness of our framework. Since weighted assumptions can be implicitly represented by multiterminal binary decision diagrams (MTBDDs), we give an >i<L>/i<*-based learning algorithm for MTBDDs to infer weighted assumptions. Experimental results suggest promising outlooks for our compositional technique.