Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems
Automatica (Journal of IFAC)
Computational approaches to reachability analysis of stochastic hybrid systems
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Verification of discrete time stochastic hybrid systems: A stochastic reach-avoid decision problem
Automatica (Journal of IFAC)
Quantitative automata model checking of autonomous stochastic hybrid systems
Proceedings of the 14th international conference on Hybrid systems: computation and control
Adaptive Gridding for Abstraction and Verification of Stochastic Hybrid Systems
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Regularization of bellman equations for infinite-horizon probabilistic properties
Proceedings of the 15th ACM international conference on Hybrid Systems: Computation and Control
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This work deals with Markov processes that are defined over an uncountable state space (possibly hybrid) and embedding non-determinism in the shape of a control structure. The contribution looks at the problem of optimization, over the set of allowed controls, of probabilistic specifications defined by automata - in particular, the focus is on deterministic finite-state automata. This problem can be reformulated as an optimization of a probabilistic reachability property over a product process obtained from the model for the specification and the model of the system. Optimizing over automata-based specifications thus leads to maximal or minimal probabilistic reachability properties. For both setups, the contribution shows that these problems can be sufficiently tackled with history-independent Markov policies. This outcome has relevant computational repercussions: in particular, the work develops a discretization procedure leading into standard optimization problems over Markov decision processes. Such procedure is associated with exact error bounds and is experimentally tested on a case study.