Technical Note: \cal Q-Learning
Machine Learning
A social reinforcement learning agent
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
IEEE Intelligent Systems
Customizing User Interaction in Smart Phones
IEEE Pervasive Computing
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Proactive and adaptive fuzzy profile control for mobile phones
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Using decision-theoretic experience sampling to build personalized mobile phone interruption models
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Personalization for unobtrusive service interaction
Personal and Ubiquitous Computing
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Ubiquitous computers, such as mobile devices, enable users to always be connected to the environment, making demands on one of the most precious resources of users: human attention. Thus, ubiquitous services should be designed in a considerate manner, demanding user attention only when it is actually required according to user needs. However, as user needs and preferences can change over time, we aim at improving the initial decisions by learning from user's feedback through experience. We present a method for adapting interaction obtrusiveness automatically based on user's reaction. Instead of asking the user to re-define his preferences about interaction obtrusiveness configurations, we learn them by means of the received feedback in a way that maximizes user's satisfaction in a long-term use.