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LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling

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.
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Submitted on : Tuesday, November 29, 2016 - 2:50:05 PM
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Mirko Fetter, Tom Gross. LiLoLe—A Framework for Lifelong Learning from Sensor Data Streams for Predictive User Modelling. 5th International Conference on Human-Centred Software Engineering (HCSE), Sep 2014, Paderborn, Germany. pp.126-143, ⟨10.1007/978-3-662-44811-3_8⟩. ⟨hal-01405070⟩



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