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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