Smartbright: Learning a Physics-Based Model of Light-Scattering With Symbolic Regression

A smart home control application allows users to interact with the Cyber-Physical System (CPS) in their living space. Conventional applications offer manual control over individual smart devices, disregarding compound effects from using multiple devices. Manual control is not user-friendly, as users must explore smart device settings until achieving their target states. In previous works, we proposed a symbolic regression approach to find settings of smart devices that achieve user-specified goals. We now demonstrate a novel application, SmartBright, an outcome-oriented light control for macOS. SmartBright allows users to set a target brightness and the current time of day; the application finds settings for the window blind and lamp to achieve this goal. We evaluated SmartBright by comparing suggested settings for a bright room against ray-traced brightness throughout the day. Additionally, we applied the user preference Do not use the lamp, if possible to minimize lamp use and evaluated these settings. Results show SmartBright suggests settings achieving user goals throughout the day, with average errors of 0.1175 P-l for the unrestricted case, and 0.1730 P-l for the restricted case.