DMCA
Training the Behaviour Preferences on Context Changes
BibTeX
@MISC{Mokhtar_trainingthe,
author = {Sonia Ben Mokhtar and Romain Rouvoy and Michael Wagner and Tiziana Margaria and Julia Padberg and Gabriele Taentzer and Kuderna-iulian Benţa and Marcel Cremene and Amalia Hoszu and Kuderna-iulian Benţa and Marcel Cremene and Amalia Hoszu},
title = {Training the Behaviour Preferences on Context Changes},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract: Personalized ambient intelligent systems should meet changes in user’s needs, which evolve over time. Our objective is to create an adaptive system that learns the user behaviour preferences. We propose *BAM – * Behaviour Adaptation Mechanism, a neural-network based control system that is trained, supervised by user’s (affective) feedback in real-time. The system deduces the preferred behaviour, based on the detection of affective state’s valence (negative, neutral and positive) from facial features analysis. The neural network is retrained periodically with the updated training set, obtained from the interpretation of the user’s reaction to the system’s decisions. We investigated how many training examples, rendered from user’s behaviour, are required in order to train the neural network so that it reaches an accuracy of at least 75%. We present the evolution of behaviour preference learning parameters when the number of context elements increases.
Keyphrases
behaviour preference context change neural network user need context element increase control system user behaviour preference user reaction system decision preferred behaviour user behaviour ambient intelligent system bam behaviour adaptation mechanism adaptive system affective state valence facial feature analysis many training example updated training
