Abstract: Smart environments possess devices that collaborate to help the user non-intrusively. One possible aid smart environment offer is to anticipate user's tasks and perform them on his/her behalf or facilitate the action completion. In this paper, we propose a framework that predicts user's actions by learning his/her behavior when interacting with the smart environment. We prepare the datasets and train a predictor that is responsible to decide whether a target transducer value should be changed or not. Our solution achieves a significant improvem...
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Topics: 
Human–computer interaction
Real-time computing
Artificial intelligence