Approximation Schemes for Infinite Linear Programs
SIAM Journal on Optimization
The Linear Programming Approach to Approximate Dynamic Programming
Operations Research
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
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)
Stochastic Optimal Control: The Discrete-Time Case
Stochastic Optimal Control: The Discrete-Time Case
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
On the connections between PCTL and dynamic programming
Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
Model checking LTL over controllable linear systems is decidable
HSCC'03 Proceedings of the 6th 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)
Satisfaction meets expectations: computing expected values of probabilistic hybrid systems with SMT
IFM'10 Proceedings of the 8th international conference on Integrated formal methods
Quantitative automata model checking of autonomous stochastic hybrid systems
Proceedings of the 14th international conference on Hybrid systems: computation and control
Linear hybrid system falsification through local search
ATVA'11 Proceedings of the 9th international conference on Automated technology for verification and analysis
Adaptive Gridding for Abstraction and Verification of Stochastic Hybrid Systems
QEST '11 Proceedings of the 2011 Eighth International Conference on Quantitative Evaluation of SysTems
Safety verification for probabilistic hybrid systems
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
| Hi-index | 0.00 |
We synthesize controllers for discrete-time stochastic hybrid systems such that the probability of satisfying a given specification on the system is maximized. The specifications are defined with finite state automata. It is shown that automata satisfaction is equivalent to a reachability problem in an extended state space consisting of the system and the automaton state spaces. The control policy is defined as a map from this extended state space to the input space. Using existing results on maximizing reachability probability, the control policy is designed to maximize probability of satisfying the specification.