Supervised learning of an abstract context model for an intelligent environment

Abstract

This paper addresses the problem of supervised learning in intelligent environments. An intelligent environment perceives user activity and offers a number of services according to the perceived information about the user. An abstract context model in the form of a situation network is used to represent the intelligent environment, its occupants and their activities. The context model consists of situations, roles played by entities and relations between these entities. The objective is to adapt the system services, which are associated to the situations of the model, to the changing needs of the user. For this, a supervisor gives feedback by correcting system services that are found to be inappropriate to user needs. The situation network can be developed by exchanging the system service-situation association, by splitting the situation, or by learning new roles. The situation split is interpreted as a replacement of the former situation by sub-situations whose number and characteristics are determined using conceptual or decision tree algorithms. Different algorithms have been tested on a context model within the SmartOffice environment of the PRIMA research group. The decision tree algorithm (ID3) has been found to give the best results.