Skip to Main content Skip to Navigation
Conference papers

Hybrid and Reinforcement Multi Agent Technology for Real Time Air Pollution Monitoring

Abstract : This paper describes the design and implementation of a modular hybrid intelligent model and system, for monitoring and forecasting of air pollution in major urban centers. It is based on Multiagent technologies, Artificial Neural Networks (ANN), Fuzzy Rule Based sub-systems and it uses a Reinforcement learning approach. A multi level architecture with a high number of agent types was employed. Multiagent’s System modular and distributed nature, allows it’s interconnection with existing systems and it reduces its functional cost, allowing its extension by incorporating decision functions and real time imposing actions capabilities.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, May 11, 2017 - 5:10:45 PM
Last modification on : Thursday, March 5, 2020 - 5:41:40 PM
Long-term archiving on: : Saturday, August 12, 2017 - 1:42:24 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Andonis Papaleonidas, Lazaros Iliadis. Hybrid and Reinforcement Multi Agent Technology for Real Time Air Pollution Monitoring. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.274-284, ⟨10.1007/978-3-642-33409-2_29⟩. ⟨hal-01521429⟩



Record views


Files downloads