Abstract
This paper deals with the idea of calculating probabilities and percentages with Petri nets. Uncertainty can be expressed with Petri nets as one place with multiple output transitions. In that case, the transition that fires are selected randomly while each transition has the same chance to fire. This paper presents the idea of assigning a weight to a transition that will be used to modify the chance at which a concurrent transition can fire. Higher weight increases the chance of firing when an uncertain situation occurs. We want to later use this to simulate university students chance to successfully complete a university course.
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References
Suraj, Z., Grochowalski, P., Bandyopadhyay, S.: Flexible generalized fuzzy petri nets for rule-based systems. In: Martín-Vide, C., Mizuki, T., Vega-Rodríguez, M.A. (eds.) TPNC 2016. LNCS, vol. 10071, pp. 196–207. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49001-4_16
Bandyopadhyay, S., Suraj, Z., Grochowalski, P.: Modified generalized weighted fuzzy petri net in intuitionistic fuzzy environment. In: Flores, V., et al. (eds.) IJCRS 2016. LNCS (LNAI), vol. 9920, pp. 342–351. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47160-0_31
Zurawski, R., Zhou, M.C.: Petri nets and industrial applications: a tutorial. IEEE Trans. Industr. Electron. 41(6), 567–583 (1994). https://doi.org/10.1109/41.334574
Suraj, Z., Bandyopadhyay, S.: Generalized weighted fuzzy petri net in intuitionistic fuzzy environment. In: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, pp. 2385–2392 (2016)
Balogh, Z., Turčáni, M.: Possibilities of modelling web-based education using IF-THEN rules and fuzzy petri nets in LMS. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasme, E. (eds.) ICIEIS 2011. CCIS, vol. 251, pp. 93–106. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25327-0_9
Klimeš, C., Balogh, Z.: Modelovanie procesov pomocou Petriho sietí. Univerzita Konštantína Filozofa v Nitre, Nitra (2012)
Zhu, D., Tan, H., Yao, S.: Petri nets-based method to elicit component-interaction related safety requirements in safety-critical systems. Comput. Electr. Eng. 71, 162–172 (2018). https://doi.org/10.1016/j.compeleceng.2018.07.019
Ferreira, C., Canhoto Neves, L., Silva, A., de Brito, J.: Stochastic Petri net-based modelling of the durability of renderings. Autom. Constr. 87, 96–105 (2018). https://doi.org/10.1016/j.autcon.2017.12.007
Emzivat, Y., Delahaye, B., Lime, D., Roux, O.H.: Probabilistic time petri nets. In: Kordon, F., Moldt, D. (eds.) PETRI NETS 2016. LNCS, vol. 9698, pp. 261–280. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39086-4_16
Katoen, J.-P., Peled, D.: Taming confusion for modeling and implementing probabilistic concurrent systems. In: Felleisen, M., Gardner, P. (eds.) ESOP 2013. LNCS, vol. 7792, pp. 411–430. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37036-6_23
Kurkovsky, S., Loganantharaj, R.: Extension of petri nets for representing and reasoning with tasks with imprecise durations. Appl. Intell. 23(2), 97–108 (2005). https://doi.org/10.1007/s10489-005-3415-8
Stencl, M., Stastny, J.: Neural network learning algorithms comparison on numerical prediction of real data. In: 16th International Conference on Soft Computing Mendel 2010, Brno, pp. 280–285 (2010)
Liu, Y., Miao, H.-K., Zeng, H.-W., Ma, Y., Liu, P.: Nondeterministic probabilistic petri net — a new method to study qualitative and quantitative behaviors of system. J. Comput. Sci. Technol. 28(1), 203–216 (2013). https://doi.org/10.1007/s11390-013-1323-7
Balogh, Z., Magdin, M., Turcani, M., Burianova, M.: Interactivity elements implementation analysis in e-courses of professional informatics subjects. In: 2011 8th International Conference on Efficiency and Responsibility in Education, Efficiency and Responsibility in Education, pp. 5–14 (2011)
Tian, Y., Wang, X., Jiang, Y., You, G.: A distributed probabilistic coverage sets configuration method for high density WSN. In: Proceedings - 2017 Chinese Automation Congress, CAC 2017, pp. 2312–2316 (2017)
Kodamana, H., Raveendran, R., Huang, B.: Mixtures of probabilistic PCA with common structure latent bases for process monitoring. IEEE Trans. Control Syst. Technol. (2017) https://doi.org/10.1109/tcst.2017.2778691
Rossi, M., Vigano, G., Moneta, D., Clerici, D.: Stochastic evaluation of distribution network hosting capacity: evaluation of the benefits introduced by smart grid technology. In: 2017 AEIT International Annual Conference: Infrastructures for Energy and ICT: Opportunities for Fostering Innovation, AEIT 2017, pp. 1–6 (2017)
Dehban, A., Jamone, L., Kampff, A.R., Santos-Victor, J.: A deep probabilistic framework for heterogeneous self-supervised learning of affordances. In: 2017 IEEE-RAS International Conference on Humanoid Robots, pp. 476–483 (2017)
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This research has been supported by University Grant Agency under the contract No. VII/12/2018 and KEGA 036UKF-4/2019.
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Kuchárik, M., Balogh, Z. (2019). Modeling of Uncertainty with Petri Nets. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11431. Springer, Cham. https://doi.org/10.1007/978-3-030-14799-0_43
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