부산대학교 도서관

The interplay between cause and effect of smart lighting devices on the perceived brightness in smart homes is an open research question. Solving it allows for outcome-oriented smart home control, for example choosing a target brightness in the living room and letting the smart home find out, what it should do to achieve this. We propose a novel method to learn the behaviour in smart homes with Symbolic Regression (SR), based on data gathered from sensors in the environment, and the actuator settings at the time of measurement. We use symbolic regression to find the dependency between the settings of smart home devices and the perceived brightness by their users. In this work we evaluate our method with a ray-traced room that includes a lamp, a window blind and the sun that assumes different positions relative to the room throughout the day. The results indicate that SR can be used for this problem, but more research and refinement needs to be performed to yield satisfying results.