Handbook of theoretical computer science (vol. B)
Handbook of logic in computer science (vol. 2)
Competitive Markov decision processes
Competitive Markov decision processes
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
A Landscape with Games in the Background
LICS '04 Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Stochastic Games with Branching-Time Winning Objectives
LICS '06 Proceedings of the 21st Annual IEEE Symposium on Logic in Computer Science
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Multi-objective model checking of Markov decision processes
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Markov decision processes with multiple objectives
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
On the controller synthesis for finite-state markov decision processes
FSTTCS '05 Proceedings of the 25th international conference on Foundations of Software Technology and Theoretical Computer Science
Strategy synthesis for markov decision processes and branching-time logics
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
Quantitative multi-objective verification for probabilistic systems
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
On Almost-Sure Properties of Probabilistic Discrete Event Systems
Fundamenta Informaticae - Theory that Counts: To Oscar Ibarra on His 70th Birthday
Electronic Notes in Theoretical Computer Science (ENTCS)
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We show that the controller synthesis and verification problems for Markov decision processes with qualitative PECTL*objectives are 2-EXPTIMEcomplete. More precisely, the algorithms are polynomialin the size of a given Markov decision process and doubly exponential in the size of a given qualitative PECTL*formula. Moreover, we show that if a given qualitative PECTL*objective is achievable by somestrategy, then it is also achievable by an effectively constructible one-counterstrategy, where the associated complexity bounds are essentially the same as above. For the fragment of qualitative PCTL objectives, we obtain EXPTIMEcompleteness and the algorithms are only singly exponential in the size of the formula.