  Since its conception, smart app market has grown exponentially. Success in
the app market depends on many factors among which the quality of the app is a
significant contributor, such as energy use. Nevertheless, smartphones, as a
subset of mobile computing devices. inherit the limited power resource
constraint. Therefore, there is a challenge of maintaining the resource while
increasing the target app quality. This paper introduces Learning Automata (LA)
as an online learning method to learn and predict the app usage routines of the
users. Such prediction can leverage the app cache functionality of the
operating system and thus (i) decreases app launch time and (ii) preserve
battery. Our algorithm, which is an online learning approach, temporally
updates and improves the internal states of itself. In particular, it learns
the transition probabilities between app launching. Each App launching instance
updates the transition probabilities related to that App, and this will result
in improving the prediction. We benefit from a real-world lifelogging dataset
and our experimental results show considerable success with respect to the two
baseline methods that are used currently for smartphone app prediction
approaches.
