Expressing Priorities and External Probabilities in Process Algebra via Mixed Open/Closed Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
Analyzing Security Protocols Using Time-Bounded Task-PIOAs
Discrete Event Dynamic Systems
Randomized consensus in expected O(n log n) individual work
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
The power of simulation relations
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
An Algebraic Framework for Defining Random Concurrent Behaviours
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Testing Finitary Probabilistic Processes
CONCUR 2009 Proceedings of the 20th International Conference on Concurrency Theory
Combining shared-coin algorithms
Journal of Parallel and Distributed Computing
Composing systems while preserving probabilities
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Automated learning of probabilistic assumptions for compositional reasoning
FASE'11/ETAPS'11 Proceedings of the 14th international conference on Fundamental approaches to software engineering: part of the joint European conferences on theory and practice of software
Formal Verification of Differential Privacy for Interactive Systems (Extended Abstract)
Electronic Notes in Theoretical Computer Science (ENTCS)
Branching bisimulation congruence for probabilistic systems
Theoretical Computer Science
Retaining the probabilities in probabilistic testing theory
FOSSACS'10 Proceedings of the 13th international conference on Foundations of Software Science and Computational Structures
Assume-Guarantee verification for probabilistic systems
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
An Algebraic Framework for Defining Random Concurrent Behaviours
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Compositional abstraction techniques for probabilistic automata
TCS'12 Proceedings of the 7th IFIP TC 1/WG 202 international conference on Theoretical Computer Science
Taming confusion for modeling and implementing probabilistic concurrent systems
ESOP'13 Proceedings of the 22nd European conference on Programming Languages and Systems
The quest for minimal quotients for probabilistic automata
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Cost preserving bisimulations for probabilistic automata
CONCUR'13 Proceedings of the 24th international conference on Concurrency Theory
Deciding bisimilarities on distributions
QEST'13 Proceedings of the 10th international conference on Quantitative Evaluation of Systems
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Probabilistic automata (PAs) constitute a general framework for modeling and analyzing discrete event systems that exhibit both nondeterministic and probabilistic behavior, such as distributed algorithms and network protocols. The behavior of PAs is commonly defined using schedulers (also called adversaries or strategies), which resolve all nondeterministic choices based on past history. From the resulting purely probabilistic structures, trace distributions can be extracted, whose intent is to capture the observable behavior of a PA. However, when PAs are composed via an (asynchronous) parallel composition operator, a global scheduler may establish strong correlations between the behavior of system components and, for example, resolve nondeterministic choices in one PA based on the outcome of probabilistic choices in the other. It is well known that, as a result of this, the (linear-time) trace distribution precongruence is not compositional for PAs. In his 1995 Ph.D. thesis, Segala has shown that the (branching-time) probabilistic simulation preorder is compositional for PAs. In this paper, we establish that the simulation preorder is, in fact, the coarsest refinement of the trace distribution preorder that is compositional. We prove our characterization result by providing (1) a context of a given PA ${\cal A}$, called the tester, which may announce the state of ${\cal A}$ to the outside world, and (2) a specific global scheduler, called the observer, which ensures that the state information that is announced is actually correct. Now when another PA ${\cal B}$ is composed with the tester, it may generate the same external behavior as the observer only when it is able to simulate ${\cal A}$ in the sense that whenever ${\cal A}$ goes to some state $s$, ${\cal B}$ can go to a corresponding state $u$, from which it may generate the same external behavior. Our result shows that probabilistic contexts together with global schedulers are able to exhibit the branching structure of PAs.