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Issue No.02 - Feb. (2013 vol.46)
pp: 46-52
Sang Wan Lee , California Institute of Technology
Oliver Prenzel , Rheinmetall Defence Electronics GmbH
Zeungnam Bien , Korea Advanced Institute of Science and Technology
ABSTRACT
IoT systems can benefit from a process model based on principles derived from the psychology and neuroscience of human behavior that emulates how humans acquire task knowledge and learn to adapt to changing context.
INDEX TERMS
Human factors, Internet of things, Knowledge representation, Context awareness, Behavioral science, User centered design, Learning systems, Ubiquitous computing, user-centered system design, Internet of Things, IoT, learning, knowledge representation, human behavior
CITATION
Sang Wan Lee, Oliver Prenzel, Zeungnam Bien, "Applying Human Learning Principles to User-Centered IoT Systems", Computer, vol.46, no. 2, pp. 46-52, Feb. 2013, doi:10.1109/MC.2012.426
REFERENCES
1. F. Kawsar, G. Kortuem, and B. Altakrouri, “Supporting Interaction with the Internet of Things across Objects, Time and Space,” Proc. Internet of Things (IoT 10), IEEE, 2010, pp. 1-8.
2. Z. Bien and S.W. Lee, “Learning Structure of Human Behavior Patterns in a Smart Home System,” Advances in Intelligent and Soft Computing, Springer, vol. 82, 2010, pp. 1-15.
3. S.W. Lee, Y.S. Kim, and Z. Bien, “A Non-Supervised Learning Framework of Human Behavior Patterns Based on Sequential Actions,” IEEE Trans. Knowledge and Data Eng., vol. 22, no. 4, 2010, pp. 479-492.
4. F. Kouneiher, S. Charron, and E. Koechlin, “Motivation and Cognitive Control in the Human Prefrontal Cortex,” Nature Neuroscience, vol. 12, no. 7, 2009, pp. 939-945.
5. O. Prenzel, “Semi-Autonomous Object Anchoring for Service-Robots,” Methods and Applications in Automation, B. Lohmann ed., Shaker Verlag GmbH, 2005, pp. 57-68.
6. O. Prenzel et al., “, Programming of Intelligent Service Robots with the Process Model FRIEND::Process and Configurable Task-Knowledge,” Robotic Systems—Applications, Control and Programming,, InTech, 2012, pp. 529-552; http://cdn.intechweb.org/pdfs27424.pdf.
7. D. Stefanov, Z. Bien, and W.-C. Bang, “The Smart House for Older Persons and Persons with Physical Disabilities: Structure, Technology Arrangements, and Perspectives,” IEEE Trans. Neural Systems and Rehabilitation Eng., vol. 12, no. 2, 2004, pp. 228-250.
8. H.-E. Lee, K.-H. Park, and Z. Bien, “Iterative Fuzzy Clustering Algorithm with Supervision to Construct Probabilistic Fuzzy Rule Base from Numerical Data,” IEEE Trans. Fuzzy Systems, vol. 16, no. 1, 2008, pp. 263-277.
9. K. Doya, “Modulators of Decision Making,” Nature Neuroscience, vol. 11, no. 4, 2008, pp. 410-416.
10. N. Daw, Y. Niv, and P. Dayan, “Uncertainty-Based Competition between Prefrontal and Dorsolateral Striatal Systems for Behavioral Control,” Nature Neuroscience, vol. 8, no. 12, 2005, pp. 1704-1711.
11. J. Gläscher et al., “States versus Rewards: Dissociable Neural Prediction Error Signals Underlying Model-Based and Model-Free Reinforcement Learning,” Neuron, vol. 66, no. 4, 2010, pp. 585-595.
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