Combining Machine Learning With Inductive Logic Learning To Detect Deviations From Daily Routines In Ambient Intelligent Environments

The availability of sensor technology in home and building automation offers new opportunities for AI applications. Machine learning (ML) methods can recognize device patterns and profiles based on sensor data and make energy-relevant predictions. At a higher level, the detected patterns can in turn be used to learn activity patterns. In this paper, we present a hybrid approach to augment ML-based outputs with inductive logic-based learning techniques. We present evaluation results and illustrate an application to agent-supported critical situation detection in the assisted living domain.