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Communications of the ACM
Compositional Methods for Probabilistic Systems
CONCUR '01 Proceedings of the 12th International Conference on Concurrency Theory
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Secure Information Flow by Self-Composition
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
LiQuor: A tool for Qualitative and Quantitative Linear Time analysis of Reactive Systems
QEST '06 Proceedings of the 3rd international conference on the Quantitative Evaluation of Systems
Switched PIOA: parallel composition via distributed scheduling
Theoretical Computer Science - Components and objects
On the verification of probabilistic I/O automata with unspecified rates
Proceedings of the 2009 ACM symposium on Applied Computing
Quantitative model checking revisited: neither decidable nor approximable
FORMATS'07 Proceedings of the 5th international conference on Formal modeling and analysis of timed systems
Qualitative analysis of partially-observable Markov decision processes
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Towards communication-based steering of complex distributed systems
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
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The verification of partial-information probabilistic systems has been shown to be undecidable in general. In this paper, we present a technique based on inspection of counterexamples that can be helpful to analyse such systems in particular cases. The starting point is the observation that the system under complete information provides safe bounds for the extremal probabilities of the system under partial information. Using classical (total information) model checkers, we can determine optimal schedulers that represent safe bounds but which may be spurious, in the sense that they use more information than is available under the partial information assumptions. The main contribution of this paper is a refinement technique that, given such a scheduler, transforms the model to exclude the scheduler and with it a whole class of schedulers that use the same unavailable information when making a decision. With this technique, we can use classical total information probabilistic model checkers to analyse a probabilistic partial information model with increasing precision. We show that, for the case of infimum reachability probabilities, the total information probabilities in the refined systems converge to the partial information probabilities in the original model.