Probabilistic Simulations for Probabilistic Processes

Segala, Roberto; Lynch, Nancy

doi:10.1007/978-3-540-48654-1_35

Cited by 362 publications

(690 citation statements)

References 9 publications

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“…Probabilistic automata (PAs) [35,36], and the closely related model of Markov decision processes (MDPs), are common formalisms for modelling systems that exhibit both probabilistic and nondeterministic behaviour. We focus here on PAs, which subsume MDPs and are particularly well suited for compositional reasoning about probabilistic systems [35].…”

Section: Modelling and Verification Of Probabilistic Systemsmentioning

confidence: 99%

“…In this paper, we discuss some recent developments in the area of automated compositional verification techniques for probabilistic systems, focusing on assume-guarantee verification techniques [29,21] for probabilistic automata [35,36], a natural model for compositional reasoning. When aiming to develop fully automated verification techniques, an important question that arises is how to devise suitable assumptions about system components.…”

Section: Introductionmentioning

confidence: 99%

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Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

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Abstract. Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from communication protocols such as Bluetooth, to nanoscale computing devices, to biological cellular processes. In order to tackle the inherent challenge of scalability, compositional approaches to verification are sorely needed. An example is assume-guarantee reasoning, where each component of a system is analysed independently, using assumptions about the other components that it interacts with. We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems. In particular, we describe techniques to automatically generate probabilistic assumptions that can be used as the basis for compositional reasoning. We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems. We also present recent improvements and extensions to this work and survey some of the promising potential directions for further research in this area.

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Section: Modelling and Verification Of Probabilistic Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

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“…weak probabilistic bisimulation) defined later. Some definitions in this subsection are motivated by or borrowed directly from [29] and [30] where classical probabilistic processes were considered. Definition 16 An execution fragment f = C 0 α 1 C 1 .…”

Section: Combined Transitionsmentioning

confidence: 99%

“…This is the reason why we need clause (2) in Definition 23. The clause (1) is originated from [21] and [29]. Definition 24 (1) Two configurations C and D are strongly bisimilar, denoted by C ∼ c D, if there is a strong probabilistic bisimulation R such that (C, D) ∈ R. (2) Two processes P and Q are strongly bisimilar, denoted by P ∼ p Q, if for any context C and any indexed setv of values, < P [v/x]; C >∼ c < Q[v/x]; C >.…”

Section: Strong Probabilistic Bisimulation Between Quantum Processesmentioning

confidence: 99%

Probabilistic bisimulations for quantum processes

Feng

Duan

et al. 2007

Information and Computation

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Modeling and reasoning about concurrent quantum systems is very important for both distributed quantum computing and quantum protocol verification. As a consequence, a general framework formally describing communication and concurrency in complex quantum systems is necessary. For this purpose, we propose a model named qCCS. It is a natural quantum extension of classical value-passing CCS which can deal with input and output of quantum states, and unitary transformations and measurements on quantum systems. The operational semantics of qCCS is given in terms of probabilistic labeled transition system. This semantics has many different features compared with the proposals in the available literature in order to describe the input and output of quantum systems which are possibly correlated with other components. Based on this operational semantics, the notions of strong probabilistic bisimulation and weak probabilistic bisimulation between quantum processes are introduced. Furthermore, some properties of these two probabilistic bisimulations, such as congruence under various combinators, are examined.

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“…The semantics of π prob is based on Probabilistic Automata, which were introduced in [13]. We briefly recall here the main notions, simplified and adapted for our needs.…”

Section: Probabilistic Automatamentioning

confidence: 99%

A Framework for Analyzing Probabilistic Protocols and Its Application to the Partial Secrets Exchange

Chatzikokolakis

Palamidessi

2005

Trustworthy Global Computing

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Abstract. We propose a probabilistic variant of the pi-calculus as a framework to specify randomized security protocols and their intended properties. In order to express an verify the correctness of the protocols, we develop a probabilistic version of the testing semantics. We then illustrate these concepts on an extended example: the Partial Secret Exchange, a protocol which uses a randomized primitive, the Oblivious Transfer, to achieve fairness of information exchange between two parties.

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Probabilistic Simulations for Probabilistic Processes

Cited by 362 publications

References 9 publications

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Probabilistic bisimulations for quantum processes

A Framework for Analyzing Probabilistic Protocols and Its Application to the Partial Secrets Exchange

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