Abstract
Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.
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De Paola, A., Gaglio, S., Lo Re, G., Ortolani, M. (2011). Multi-sensor Fusion through Adaptive Bayesian Networks. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_33
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DOI: https://doi.org/10.1007/978-3-642-23954-0_33
Publisher Name: Springer, Berlin, Heidelberg
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