文章基本信息

标题：Training the Behaviour Preferences on Context Changes
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作者：Kuderna-Iulian Benta ; Marcel Cremene ; Amalia Hoszu 等
期刊名称：Electronic Communications of the EASST
电子版ISSN：1863-2122
出版年度：2010
卷号：28
语种：English
出版社：European Association of Software Science and Technology (EASST)
摘要：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.