Training the Behaviour Preferences on Context Changes

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

Analysis of a hybrid neural network as underlying mechanism for a situation prediction engine

This paper presents the results regarding a technique that can be used as an underlying mechanism for situation prediction. We analysed a hybrid neural network called Multi-output Adaptive Neural Fuzzy Inference System (MANFIS) and compared its predictive ability with a Multi-Layer Perceptron (MLP). The results demonstrate that, depending on the application, the use of neural networks can be considered to be a good approach for situation prediction, when combined with other techniques.Key words: situation, context, prediction, neural networks, MANFIS