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Machine Learning methods for optimization in Multi-Agent Decision Support System : application to Sign Placement for Tsunami Evacuation

Abstract : In recent years, whenever we talked about tsunami, we mentioned the terrible destruction and huge casualties (the tsunami from Indian Ocean in 2004 and the tsunami in Tohoku Japan 2011). The evacuation is the most effective solution to save people in this kind of disaster. Before a tsunami arrival, people should go to the high buildings (called vertical shelters) or high ground areas or zones far from the sea (called horizontal shelters). However, there are always the part of evacuees (e.g. the tourist) who lack information of the city map, we then focus on the solution to guide people in evacuation.This thesis presents the approach of Efficient Optimization in a Multi-Agent Decision Support System : Application to Sign Placement for Tsunami Evacuation. More precisely, we study the approach to place signs and also evacuation maps in the city (at certain crossroads or junctions) to have as many people (call survivors) as possible reach the shelters before tsunami arrival. Our multiagent simulator allows us to estimate the number of survivors after a tsunami. Using various optimization techniques, we then place signs in the city in order to maximize the estimated number of survivors
Keywords : Decision system
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Van-Minh Le. Machine Learning methods for optimization in Multi-Agent Decision Support System : application to Sign Placement for Tsunami Evacuation. Multiagent Systems [cs.MA]. Université Sorbonne Paris Cité, 2016. English. ⟨NNT : 2016USPCD097⟩. ⟨tel-02352366⟩

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