Motivated Reinforcement Learning

Name: Motivated Reinforcement Learning
Brand: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
SKU: 9783540891864
Price: 582.66 RON
Availability: InStock

Curious Characters for Multiuser Games

Autor: Kathryn Merrick, Mary L. Maher

Limbă:

Legare: Copertă tare

Editura: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Disponibilitate: La editor doar la comandă

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582.66 lei

Motivated learning is an emerging research field in artificial intelligence and cognitive modelling....

Informații despre carte

Autor

Kathryn Merrick, Mary L. Maher

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2008

Pagini

206

EAN

9783540891864

ISBN

3540891862

Enbook ID

01570976

Editura

Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Greutate

512

Dimensiuni

155 x 235 x 19

Descriere completă

Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment.§This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. §Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems in particular multiuser, online games.

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Motivated Reinforcement Learning

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