Probabilistic Programming Process Algebra

Georgoulas, Anastasis; Hillston, Jane; Milios, Dimitrios; Sanguinetti, Guido

doi:10.1007/978-3-319-10696-0_21

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…The CARMA Specification Language provides a wrapper around the CARMA process calculus adding non-essential (but useful) features such as data types and data structures, functions, and the ability to specify real-valued measures of interest over the model. In some modelling languages measures of interest or Markov reward structures are defined externally to the model but in CARMA and languages such as CASPA [10], PRISM [11] and ProPPA [6], the specification of measures of interest and reward structures is included in the modelling language itself. Given a CARMA specification, the CARMA Eclipse Plug-in compiles the model into a set of Java classes which are linked with the CARMA simulator classes to provide a custom simulator for this model.…”

Section: Analysis and Resultsmentioning

confidence: 99%

Modelling movement for collective adaptive systems with CARMA

Zoń

Galpin²,

Gilmore³

2016

Electron. Proc. Theor. Comput. Sci.

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Space and movement through space play an important role in many collective adaptive systems (CAS). CAS consist of multiple components interacting to achieve some goal in a system or environment that can change over time. When these components operate in space, then their behaviour can be affected by where they are located in that space. Examples include the possibility of communication between two components located at different points, and rates of movement of a component that may be affected by location. The CARMA language and its associated software tools can be used to model such systems. In particular, a graphical editor for CARMA allows for the specification of spatial structure and generation of templates that can be used in a CARMA model with space. We demonstrate the use of this tool to experiment with a model of pedestrian movement over a network of paths.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

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Section: Analysis and Resultsmentioning

confidence: 99%

Modelling movement for collective adaptive systems with CARMA

Zoń

Galpin²,

Gilmore³

2016

Electron. Proc. Theor. Comput. Sci.

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“…They have also been used for modular decomposition of systems in parameter estimation tasks [42] Finally, the integration of machine learning and formal methods is happening also at the level of modelling languages. In [43], a novel process algebra is defined, with a semantics in terms of uncertain CTMC, and equipped with inference routines to reduce parametric uncertainty in presence of observations.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Methods in Statistical Model Checking and System Design – Tutorial

Bortolussi

Milios

Sanguinetti

2015

Runtime Verification

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at TU Wien, Vienna, Austria.The RV series is an annual meeting that gathers together scientists from both academia and industry interested in investigating novel lightweight formal methods to monitor, analyze, and guide the execution of programs. The discussion centers around two main aspects. The first is to understand whether the runtime verification techniques can practically complement the traditional methods for proving programs correct before their execution, such as model checking and theorem proving. The second concerns formal methods and how their application can improve traditional ad-hoc monitoring techniques used in performance monitoring, hardware design emulation, etc.RV started in 2001 as an annual workshop and turned into a conference in 2010. The workshops were organized as satellite events to an established forum, including CAV and ETAPS. The proceedings for RV from 2001 to 2005 were published in the

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“…For our purposes we must move from this purely non-deterministic setting to a probabilistic setting, where, instead of a binary decision, we have quantitative information about our belief in the plausibility of a value. This leads to the de nition of a Probabilistic Constraint Markov Chain (PCMC) [18].…”

Section: Proppa Semanticsmentioning

confidence: 99%

“…ProPPA was rst presented in [18] as a process algebra with basic inference capabilities. Here we recall the syntax and semantics of the language and give a detailed account of the sophisticated ACM Transactions on Modeling and Computer Simulation, Vol.…”

Section: Introductionmentioning

confidence: 99%

ProPPA

Georgoulas

Hillston

Sanguinetti

2018

ACM Trans. Model. Comput. Simul.

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Formal languages like process algebras have been shown to be e ective tools in modelling a wide range of dynamic systems, providing a high-level description that is readily transformed into an executable model. However their application is sometimes hampered because the quantitative details of many real-world systems of interest are not fully known. In contrast, in machine learning there has been work to develop probabilistic programming languages, which provide system descriptions that incorporate uncertainty and leverage advanced statistical techniques to infer unknown parameters from observed data. Unfortunately current probabilistic programming languages are typically too low-level to be suitable for complex modelling. In this paper we present ProPPA, the rst instance of the probabilistic programming paradigm being applied to a high-level, formal language, and its supporting tool suite. We explain the semantics of the language in terms of a quantitative generalisation of Constraint Markov Chains and describe the implementation of the language, discussing in some detail the di erent inference algorithms available, and their domain of applicability. We conclude by illustrating the use of the language on simple but non-trivial case studies: here ProPPA is shown to combine the elegance and simplicity of high-level formal modelling languages with an e ective way of incorporating data, making it a promising tool for modelling studies. CCS Concepts: • Computing methodologies → Modeling methodologies; Uncertainty quanti cation; • Mathematics of computing → Markov processes; Bayesian computation; Markov-chain Monte Carlo methods;

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Probabilistic Programming Process Algebra

Cited by 9 publications

References 22 publications

Modelling movement for collective adaptive systems with CARMA

Modelling movement for collective adaptive systems with CARMA

Machine Learning Methods in Statistical Model Checking and System Design – Tutorial

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