Abstract
In the Internet of Things (IoT) domain, being able to propose a contextualized and personalized user experience is a major issue. The explosion of connected objects makes it possible to gather more and more information about users and therefore create new, more innovative services that are truly adapted to users. To attain these goals, and meet the user expectations, applications must learn from user behavior and continuously adapt this learning accordingly. To achieve this, we propose a solution that provides a simple way to inject this kind of behavior into IoT applications by pairing a learning algorithm (C4.5) with Behavior Trees. In this context, this paper presents new forgetting methods for the C4.5 algorithm in order to continuously adapt the learning.
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D’Amato, A., Boussard, M. (2016). Forgetting Methods for White Box Learning. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_19
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DOI: https://doi.org/10.1007/978-3-319-40159-1_19
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