The complexity of Markov decision processes
Mathematics of Operations Research
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Non-randomized strategies in stochastic decision processes
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On the complexity of partially observed Markov decision processes
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
Dynamic Programming and Stochastic Control
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POPL '83 Proceedings of the 10th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
On the Complexity of Finite Memory Policies for Markov Decision Processes
MFCS '95 Proceedings of the 20th International Symposium on Mathematical Foundations of Computer Science
Proceedings of the 12th Colloquium on Automata, Languages and Programming
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CONCUR '99 Proceedings of the 10th International Conference on Concurrency Theory
Model Checking of Probabalistic and Nondeterministic Systems
Proceedings of the 15th Conference on Foundations of Software Technology and Theoretical Computer Science
Verifying Automata Specifications of Probabilistic Real-time Systems
Proceedings of the Real-Time: Theory in Practice, REX Workshop
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COCO '98 Proceedings of the Thirteenth Annual IEEE Conference on Computational Complexity
Model checking for a probabilistic branching time logic with fairness
Distributed Computing
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
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We prove that given a Markov Decision Process (MDP) and a fixed subset of its states~F, there is a Markov policy which maximizes everywhere the probability to reach F infinitely often. Moreover such a maximum policy is computable in polytime in the size of the MDP. This result can be applied in order to control a system with randomized or uncertain behavior with respect to a given property to optimize.