Abstract : Adaptation in context-aware ubiquitous environments and adaptive systems is becoming more and more complex. Adaptations need to take into account information from a plethora of heterogeneous sensors, while the adaptation decisions often imply personalised aspects and individual preferences, which are likely to change over time. We present a novel concept for lifelong learning from sensor data streams for predictive user modelling that is applicable in scenarios where simpler mechanisms that rely on pre-trained general models fall short. With the LiLoLe-Framework, we pursue an approach that allows ubiquitous systems to continuously learn from their users and adapt the system at the same time through stream-based active learning. This Framework can guide the development of context-aware or adaptive systems in form of an overall architecture.
Stefan Sauer; Cristian Bogdan; Peter Forbrig; Regina Bernhaupt; Marco Winckler. 5th International Conference on Human-Centred Software Engineering (HCSE), Sep 2014, Paderborn, Germany. Springer, Lecture Notes in Computer Science, LNCS-8742, pp.126-143, 2014, Human-Centered Software Engineering. 〈10.1007/978-3-662-44811-3_8〉
https://hal.inria.fr/hal-01405070
Contributeur : Hal Ifip
<>
Soumis le : mardi 29 novembre 2016 - 14:50:05
Dernière modification le : mardi 29 novembre 2016 - 15:02:05
Document(s) archivé(s) le : lundi 27 mars 2017 - 08:44:09
Mirko Fetter, Tom Gross. LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling. Stefan Sauer; Cristian Bogdan; Peter Forbrig; Regina Bernhaupt; Marco Winckler. 5th International Conference on Human-Centred Software Engineering (HCSE), Sep 2014, Paderborn, Germany. Springer, Lecture Notes in Computer Science, LNCS-8742, pp.126-143, 2014, Human-Centered Software Engineering. 〈10.1007/978-3-662-44811-3_8〉. 〈hal-01405070〉
