Technical systems interacting with the real world can be elegantly modelled using probabilistic hybrid automata (PHA). Parametric probabilistic hybrid automata are dynamical systems featuring hybrid discrete-continuous dynamics and parametric probabilistic branching, thereby generalizing PHA by capturing a family of PHA within a single model. Such systems have a broad range of applications, fro...

Interactions between the medical devices and the human body in Medical Cyber Physical Systems (MCPSes) are considered for verifying patient’s safety. The discrete and continuous dynamics of an MCPS require a hybrid approach towards modeling and analysis. In this regard, hybrid automata is used to model analgesic infusion pumps, an exemplary MCPS application. Excursions of unsafe states in this ...

Markov automata are a powerful formalism for modelling systems which exhibit nondeterminism, probabilistic choices and continuous stochastic timing. We consider the computation of long-run average rewards, the most classical problem in continuous-time Markov model analysis. We propose an algorithm based on value iteration. It improves the state of the art by orders of magnitude. The contributio...

Inductive learning is the method of learning from observations. Inductive learning has important applications over a wide range of area including pattern recognition, language acquisition, bio-informatics and intelligent agent design. Because of such diverse applicability, inductive learning methods including automata learning, grammar induction, hidden markov model learning and symbolic statis...

Though the hybrid system model checker HYTECH has successfully verified some systems, it restricts the dynamics to linear hybrid automata. We have designed an algorithm capable of verifying systems with more general dynamics. This algorithm uses interval numerical methods to conservatively overapproximate the reachable states of a hybrid automaton. We have implemented our new algorithm in HYTEC...