Authors:
Anthony Stein
1
;
Stefan Rudolph
1
;
Sven Tomforde
2
and
Jörg Hähner
1
Affiliations:
1
Organic Computing Group and University of Augsburg, Germany
;
2
Intelligent Embedded Systems and University of Kassel, Germany
Keyword(s):
Organic Computing, Self-x, Self-learning, Self-adaptive, Reinforcement Learning, Smart Camera, Smart Camera Network, Surveillance Camera, Learning Classifier System, Extended Classifier System, Q-learning.
Abstract:
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.