Self-learning Smart Cameras - Harnessing the Generalization Capability of XCS
Anthony Stein, Stefan Rudolph, Sven Tomforde, Jörg Hähner · 2017
In this paper, we show how an evolutionary rule-based machine learning technique can be applied to tackle the task of self-configuration of smart camera networks.More precisely, the Extended Classifier System (XCS) is utilized to learn a configuration strategy for the pan, tilt, and zoom of smart cameras.Thereby, we extend our previous approach, which is based on Q-Learning, by harnessing the generalization capability of Learning Classifier Systems (LCS), i.e. avoiding to separately approximate the quality of each possible (re-)configuration (action) in reaction to a certain situation (state).Instead, situations in which the same reconfiguration is adequate are grouped to one single rule.We demonstrate that our XCS-based approach outperforms the Q-learning method on the basis of empirical evaluations on scenarios of different severity.