Motivated Reinforcement Learning: Curious Characters for Multiuser Games - Hardcover

Merrick, Kathryn E.; Maher, Mary Lou

Hardcover

ISBN 10: 3540891862 ISBN 13: 9783540891864

Publisher: Springer, 2009

Synopsis

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.

About this title

Synopsis

Motivated learning is an emerging research field in artificial intelligence and cognitive modeling research. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments, the goal being to understand how machines could develop new skills and achieve goals that were not predefined by human engineers. In particular, motivated reinforcement learning agents allow the design of nonplayer computer game characters that adapt their behaviour in response to changes in their environment.This book covers the design, evaluation and application of computational models of motivation in reinforcement learning. The authors start with overviews of motivation, reinforcement learning, and modelling motivation, and then explain performance metrics, scalability in complex and dynamic environments, and applications in simulated game worlds and open-ended virtual worlds. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems, particularly multiuser online games.

Edition details

• Publisher: Springer
• Publication date: 2009
• Language: English
• ISBN 10: 3540891862
• ISBN 13: 9783540891864
• Binding: Hardcover
• Number of pages: 220