In this letter, we propose an incremental abstraction method for dynamically over-approximating nonlinear systems in a bounded domain by solving sequence of linear programs, resulting affine upper and lower hyperplanes with expanding operating regions. Although the problem can be solved using single program, existing approaches suffer from computation space complexity that grows exponentially s...

Abstract Markov decision processes are a ubiquitous formalism for modelling systems with non-deterministic and probabilistic behavior. Verification of these models is subject to the famous state space explosion problem. We alleviate this problem by exploiting hierarchical structure repetitive parts. This not only occurs naturally in robotics, but also programs describing, e.g., network protocol...