The complexity of Markov decision processes
Mathematics of Operations Research
The complexity of probabilistic verification
Journal of the ACM (JACM)
Modeling and verification of randomized distributed real-time systems
Modeling and verification of randomized distributed real-time systems
Languages, automata, and logic
Handbook of formal languages, vol. 3
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Model Checking of Probabalistic and Nondeterministic Systems
Proceedings of the 15th Conference on Foundations of Software Technology and Theoretical Computer Science
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Automata logics, and infinite games: a guide to current research
Automata logics, and infinite games: a guide to current research
Probabilistic symbolic model checking with PRISM: a hybrid approach
International Journal on Software Tools for Technology Transfer (STTT) - Special section on tools and algorithms for the construction and analysis of systems
Partial Order Reduction for Probabilistic Systems
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Partial Order Reduction on Concurrent Probabilistic Programs
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Model checking for a probabilistic branching time logic with fairness
Distributed Computing
Recognizing ?-regular Languages with Probabilistic Automata
LICS '05 Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
Switched PIOA: parallel composition via distributed scheduling
Theoretical Computer Science - Components and objects
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Power of Randomization in Automata on Infinite Strings
CONCUR 2009 Proceedings of the 20th International Conference on Concurrency Theory
Partial Order Reduction for Probabilistic Systems: A Revision for Distributed Schedulers
CONCUR 2009 Proceedings of the 20th International Conference on Concurrency Theory
Quantitative Analysis under Fairness Constraints
ATVA '09 Proceedings of the 7th International Symposium on Automated Technology for Verification and Analysis
Quantitative model checking revisited: neither decidable nor approximable
FORMATS'07 Proceedings of the 5th international conference on Formal modeling and analysis of timed systems
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
Information Hiding in Probabilistic Concurrent Systems
QEST '10 Proceedings of the 2010 Seventh International Conference on the Quantitative Evaluation of Systems
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The automata-based model checking approach for randomized distributed systems relies on an operational interleaving semantics of the system by means of a Markov decision process and a formalization of the desired event E by an ω-regular linear-time property, e.g., an LTL formula. The task is then to compute the greatest lower bound for the probability for E that can be guaranteed even in worst-case scenarios. Such bounds can be computed by a combination of polynomially time-bounded graph algorithm with methods for solving linear programs. In the classical approach, the "worst-case" is determined when ranging over all schedulers that decide which action to perform next. In particular, all possible interleavings and resolutions of other nondeterministic choices in the system model are taken into account. The worst-case analysis relying on this general notion of schedulers is often too pessimistic and leads to extreme probability values that can be achieved only by schedulers that are unrealistic for parallel systems. This motivates the switch to more realistic classes of schedulers that respect the fact that the individual processes only have partial information about the global system states. Such classes of partial-information schedulers yield more realistic worst-case probabilities, but computationally they are much harder. A wide range of verification problems turns out to be undecidable when the goal is to check that certain probability bounds hold under all partial-information schedulers.