machine learning based decision support (13 résultats)

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 208 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chu Thanh-QuangWas 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.

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 208 pp. Englisch.

Taschenbuch. Etat : Neu. 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 Print on Demand.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.