Design of an ecology of activity-aware cells in ambient intelligence environments
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Date
2012Type
- Conference Paper
ETH Bibliography
yes
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Abstract
The next generation of ambient intelligence environments will support people throughout their lives with much greater autonomy and capacity to assist than nowadays. To achieve this, a shift is required away from one mainstream view in ubiquitous computing: the “handcrafting” of extremely application specific and narrowly defined “context-aware assistants”. Instead, ambient intelligence and physically embedded intelligent systems (PEIS) will converge. A key for this to happen is for PEIS to be context-aware: they must especially be able to infer the activities, behaviors, intentions, and even emotions of users in their daily life. As the environment can be very dynamic, with changing resources or user expectations, so must the PEIS. We approach this problem by an ecology of Context Cells: items with communication and processing capabilities geared at discovering and classifying patterns of human behaviors. The physica implementation of a Context Cell is a situated sensor-actuator node. Individual “specialized” cells are independently capable of context awareness, and capable of adaptation. Cells cooperate with each other to recognize more complex pieces of context, increase performance, or increase robustness to faults. Finally, cells can train other “undifferentiated” cells which are newly discovered in the ambient intelligence environment. This allows to extend the coverage of context awareness through the environment by spreading capabilities among cells. The ecology of Context Cells forms the substrate for ambient intelligence environments capable of lifelong adaptation and evolution guided by the user's expectations. Show more
Publication status
publishedExternal links
Book title
10th IFAC Symposium on Robot Control, SYROCO 2012. PreprintsJournal / series
IFAC Proceedings VolumesVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Human machine interface; Autonomous control; Learning systemsOrganisational unit
03388 - Tröster, Gerhard (emeritus)
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ETH Bibliography
yes
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