Agent-based models and machine learning in decision support systems: Application to resource allocation in situations of urban catastrophes - Couverture souple

Chu, Thanh-Quang

 
9783659673252: Agent-based models and machine learning in decision support systems: Application to resource allocation in situations of urban catastrophes
Couverture souple
ISBN 10 : 3659673250 ISBN 13 : 9783659673252
Editeur : LAP LAMBERT Academic Publishing, 2015

Synopsis

The context of this book is the development of a spatial decision support system to train and support people in dealing with the vital problem of resource allocation for disaster response activities in urban areas. The goal is to design a method that combines agent-based models, geographical information systems, participatory design and machine learning to address three points: building rescue simulations that integrate all available information and behaviors; enabling stakeholders to interact directly with the agents of the simulation in order to teach them relevant behaviors; designing a learning mechanism to acquire these behaviors through an automated, online and interactive way, and to effectively translate the knowledge and experience that stakeholders use in organizing rescue missions into agents behaviors. This approach not only improves the (simulated) effectiveness of rescue activities but also makes the simulation model more realistic. It allows stakeholders to acquire better understanding of rescue issues and become more experienced in emergency response. The tools & methodologies designed can be quite easily generalized to other contexts in which similar issues arise.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

The context of this book is the development of a spatial decision support system to train and support people in dealing with the vital problem of resource allocation for disaster response activities in urban areas. The goal is to design a method that combines agent-based models, geographical information systems, participatory design and machine learning to address three points: building rescue simulations that integrate all available information and behaviors; enabling stakeholders to interact directly with the agents of the simulation in order to teach them relevant behaviors; designing a learning mechanism to acquire these behaviors through an automated, online and interactive way, and to effectively translate the knowledge and experience that stakeholders use in organizing rescue missions into agents behaviors. This approach not only improves the (simulated) effectiveness of rescue activities but also makes the simulation model more realistic. It allows stakeholders to acquire better understanding of rescue issues and become more experienced in emergency response. The tools & methodologies designed can be quite easily generalized to other contexts in which similar issues arise.

Biographie de l'auteur

Was born in Vietnam in 1981, I finished Engineer degree in Computer Science at Hanoi University of Technology and obtained Master degree at French Speaking Institute for Computer Science, Hanoi. In 2011, I successfully defended my PhD thesis of University Pierre and Marie Curie in Paris. At the moment, I work as research engineer at VNG Corp. Hanoi.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2015
Langue
anglais
ISBN 10 
3659673250
ISBN 13 
9783659673252
Reliure
Broché
Nombre de pages
208
Coordonnées du fabricant
non disponible
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