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

Thanh-Quang Chu

ISBN 10: 3659673250 / ISBN 13: 9783659673252

Published by LAP LAMBERT Academic Publishing, 2015

About this Item

Agent-based models and machine learning in decision support systems | Application to resource allocation in situations of urban catastrophes | Thanh-Quang Chu | Taschenbuch | 208 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659673252 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Seller Inventory # 104899972

Bibliographic Details

• Title: Agent-based models and machine learning in decision support systems | Application to resource allocation in situations of urban catastrophes
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2015
• Language: English
• ISBN 10: 3659673250
• ISBN 13: 9783659673252
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 14D millimeters 220 millimeters width by 150 millimeters height by 14 millimeters depth
• Weight: 328 grams
• Seller catalogs:
  • Bücher

About this title

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.

About the Author

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.