Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction
Keywords:Context prediction, logic, PCTL, pervasive system, context-aware system, stochastic, transition model
AbstractContext 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.
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How to Cite
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. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/2018_6_1411