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Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges

Abstract : Software engineering has been historically topdown. From a fully specified problem, a software engineer needs to detail each step of the resolution to get a solution. The resulting program will be functionally adequate as long as its execution environment complies with the original specifications. With their large amount of data and their ever changing multi-level dynamics, smart cities are too complex for a topdown approach. They prompt the need for a paradigm shift in computer science. Programs should be able to self-adapt on the fly, to handle unspecified events, and to efficiently deal with tremendous amount of data. To this end, bottom-up approach should become the norm. Machine learning is a first step, and distributed computing helps. Multi-Agent Systems (MAS) can combine machine learning and distributed computing and may be easily designed with a bottom-up approach. This paper explores how MASs can answer challenges at various levels of smart cities, from sensors networks to ambient intelligence.
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Submitted on : Wednesday, March 8, 2017 - 9:47:05 AM
Last modification on : Friday, June 19, 2020 - 3:35:27 AM
Long-term archiving on: : Friday, June 9, 2017 - 12:53:02 PM


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  • HAL Id : hal-01484992, version 1
  • OATAO : 17122


Julien Nigon, Estèle Glize, David Dupas, Fabrice Crasnier, Jérémy Boes. Use Cases of Pervasive Artificial Intelligence for Smart Cities Challenges. IEEE Workshop on Smart and Sustainable City (WSSC 2016) associated to the International Conference IEEE UIC 2016, Jul 2016, Toulouse, France. pp. 1021-1027. ⟨hal-01484992⟩



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