Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction

Year, pages: 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.

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

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

Publisher: Ústav informatiky SAV