The complexity of stochastic games
Information and Computation
The complexity of probabilistic verification
Journal of the ACM (JACM)
Competitive Markov decision processes
Competitive Markov decision processes
Languages, automata, and logic
Handbook of formal languages, vol. 3
Quantitative solution of omega-regular games380872
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Automata on Infinite Objects and Church's Problem
Automata on Infinite Objects and Church's Problem
MFCS '00 Proceedings of the 25th International Symposium on Mathematical Foundations of Computer Science
Universal games of incomplete information
STOC '79 Proceedings of the eleventh annual ACM symposium on Theory of computing
Theoretical Computer Science
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Qualitative Determinacy and Decidability of Stochastic Games with Signals
LICS '09 Proceedings of the 2009 24th Annual IEEE Symposium on Logic In Computer Science
On decision problems for probabilistic Büchi automata
FOSSACS'08/ETAPS'08 Proceedings of the Theory and practice of software, 11th international conference on Foundations of software science and computational structures
Qualitative analysis of partially-observable Markov decision processes
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
| Hi-index | 0.00 |
We introduce games with probabilistic uncertainty, a model for controller synthesis in which the controller observes the state through imprecise sensors that provide correct information about the current state with a fixed probability. That is, in each step, the sensors return an observed state, and given the observed state, there is a probability distribution (due to the estimation error) over the actual current state. The controller must base its decision on the observed state (rather than the actual current state, which it does not know). On the other hand, we assume that the environment can perfectly observe the current state. We show that controller synthesis for qualitative ω-regular objectives in our model can be reduced in polynomial time to standard partial-observation stochastic games, and vice-versa. As a consequence we establish the precise decidability frontier for the new class of games, and establish optimal complexity results for all the decidable problems.