Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction

Rok, strany: 2019, 1411 - 1442

Context prediction is a promoting research topic with a lot of challenges and opportunities. Indeed, with the constant evolution of context-aware systems, context prediction remains a complex task due to the lack of formal approach. In this paper, we propose a new approach to enhance context prediction using a probabilistic temporal logic and model checking. The probabilistic temporal logic PCTL is used to provide an efficient expressivity and a reasoning based on temporal logic in order to fit with the dynamic and non-deterministic nature of the system's environment. Whereas, the probabilistic model checking is used for automatically verifying that a probabilistic system satisfies a property with a given likelihood. Our new approach allows a formal expressivity of a multidimensional context prediction. Tested on real data our model was able to achieve 78 % of the future activities prediction accuracy.

ISO 690:

Ameyed, D., Miraoui, M., Zaguia, A., Jaafar, F., Tadj, C. 2019. Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction. In Computing and Informatics, vol. 37, no.6, pp. 1411-1442. 1335-9150. DOI: https://doi.org/10.4149/cai_2018_6_1411

APA:

Ameyed, D., Miraoui, M., Zaguia, A., Jaafar, F., Tadj, C. (2019). Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction. Computing and Informatics, 37(6), 1411-1442. 1335-9150. DOI: https://doi.org/10.4149/cai_2018_6_1411

Vydavateľ: Ústav informatiky SAV