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An intelligent lighting system: learn user preferences from inconsistent feedback

In this paper, we propose an intelligent lighting system that is able to learn the most preferred light level of a user by iteratively interacting with the user. In each iteration, the system offers the user a certain amount of light and the user gives his feedback to the system. When the user is satisfied, implicit feedback is given by not intervening the system. Otherwise explicit feedback such as "too bright" or "too dark" will be given. Each user feedback is modeled as a probabilistic event, which means that it is possible that sometimes the user will give different feedback for the same light level. To avoid disturbing the user to much, we propose a learning algorithm which learns the most preferred light level of the user with only a few explicit interventions.