Abstract
Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the usefulness of these elegant tools in many real world applications. Here we introduce ProPPA, a process algebra which incorporates uncertainty in the model description, allowing the use of Machine Learning techniques to incorporate observational information in the modelling. We define the semantics of the language by introducing a quantitative generalisation of Constraint Markov Chains. We present results from a prototype implementation of the language, demonstrating its usefulness in performing inference in a non-trivial example.
Keywords
- Model Check
- Inference Algorithm
- Label Transition System
- Approximate Bayesian Computation
- Process Algebra
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aldini, A., Bernardo, M., Corradini, F.: A process algebraic approach to software architecture design. Springer (2010)
Aziz, A., Sanwal, K., Singhal, V., Brayton, R.: Verifying continuous time Markov chains. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102, pp. 269–276. Springer, Heidelberg (1996)
Baldan, P., Bracciali, A., Brodo, L., Bruni, R.: Deducing Interactions in Partially Unspecified Biological Systems. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) AB 2007. LNCS, vol. 4545, pp. 262–276. Springer, Heidelberg (2007)
Bortolussi, L., Sanguinetti, G.: Learning and designing stochastic processes from logical constraints. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 89–105. Springer, Heidelberg (2013)
Brim, L., Češka, M., Dražan, S., Šafránek, D.: Exploring parameter space of stochastic biochemical systems using quantitative model checking. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 107–123. Springer, Heidelberg (2013)
Caillaud, B., Delahaye, B., Larsen, K.G., Legay, A., Pedersen, M.L., Wsowski, A.: Constraint Markov Chains. Theor. Comp. Science 412(34), 4373–4404 (2011)
Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S.: Machine Learning Biochemical Networks from Temporal Logic Properties. In: Priami, C., Plotkin, G. (eds.) Trans. on Comput. Syst. Biol. VI. LNCS (LNBI), vol. 4220, pp. 68–94. Springer, Heidelberg (2006)
Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of biological systems. Theor. Comp. Science 410(33-34), 3065–3084 (2009)
Clarke, E.M., Emerson, E.A., Sistla, A.P.: Automatic verification of finite-state concurrent systems using temporal logic specifications. ACM Trans. Program. Lang. Syst. 8(2), 244–263 (1986)
Daley, D.J., Kendall, D.G.: Epidemics and Rumours. Nature 204(4963) (1964)
Galpin, V.: Equivalences for a biological process algebra. Theor. Comp. Science 412(43), 6058–6082 (2011)
Georgoulas, A., Hillston, J., Sanguinetti, G.: ABC–Fun: A Probabilistic Programming Language for Biology. In: Gupta, A., Henzinger, T.A. (eds.) CMSB 2013. LNCS, vol. 8130, pp. 150–163. Springer, Heidelberg (2013)
Goodman, N.D., Mansinghka, V.K., Roy, D.M., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: McAllester, D.A., Myllymäki, P. (eds.) UAI, pp. 220–229. AUAI Press (2008)
Hermanns, H.: Interactive Markov Chains: and the quest for quantified quality. Springer (2002)
Hillston, J.: A Compositional Approach to Performance Modelling. CUP (1996)
Jha, S.K., Clarke, E.M., Langmead, C.J., Legay, A., Platzer, A., Zuliani, P.: A Bayesian Approach to Model Checking Biological Systems. In: Degano, P., Gorrieri, R. (eds.) CMSB 2009. LNCS, vol. 5688, pp. 218–234. Springer, Heidelberg (2009)
Marco, D., Cairns, D., Shankland, C.: Optimisation of process algebra models using evolutionary computation. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1296–1301 (2011)
Marco, D., Shankland, C., Cairns, D.: Evolving Bio-PEPA process algebra models using genetic programming. In: Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference, GECCO 2012, New York, NY, USA, pp. 177–184 (2012)
Minka, T., Winn, J., Guiver, J., Knowles, D.: Infer.NET 2.5, Microsoft Research Cambridge (2012), http://research.microsoft.com/infernet
de Nicola, R., Latella, D., Loreti, M., Massink, M.: A Uniform Definition of Stochastic Process Calculi. ACM Comput. Surv. 46(1), 5:1–5:35 (2013)
Pfeffer, A.: The Design and Implementation of IBAL: A General-Purpose Probabilistic Language. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning. The MIT Press (2007)
Pfeffer, A.: CTPPL: A Continuous Time Probabilistic Programming Language. In: IJCAI, pp. 1943–1950 (2009)
Sciacca, E., Spinella, S., Calcagno, C., Damiani, F., Coppo, M.: Parameter Identification and Assessment of Nutrient Transporters in AM Symbiosis through Stochastic Simulations. ENTCS 293, 83–96 (2013), Proceedings of CS2Bio 2012
Sen, K., Viswanathan, M., Agha, G.: Model-Checking Markov Chains in the Presence of Uncertainties. In: Hermanns, H., Palsberg, J. (eds.) TACAS 2006. LNCS, vol. 3920, pp. 394–410. Springer, Heidelberg (2006)
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M.P.: Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of The Royal Society Interface 6(31), 187–202 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Georgoulas, A., Hillston, J., Milios, D., Sanguinetti, G. (2014). Probabilistic Programming Process Algebra. In: Norman, G., Sanders, W. (eds) Quantitative Evaluation of Systems. QEST 2014. Lecture Notes in Computer Science, vol 8657. Springer, Cham. https://doi.org/10.1007/978-3-319-10696-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-10696-0_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10695-3
Online ISBN: 978-3-319-10696-0
eBook Packages: Computer ScienceComputer Science (R0)