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Motivated Reinforcement Learning : Curious Characters for Multiuser Games - Kathryn E. Merrick

Motivated Reinforcement Learning

Curious Characters for Multiuser Games

By: Kathryn E. Merrick, Mary Lou Maher

eText | 12 June 2009

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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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Non-FictionComputing & I.T.Graphical & Digital Media ApplicationsComputer ScienceArtificial IntelligenceComputer VisionDigital Lifestyle & Online World: Consumer & User GuidesInternet Guides & Online ServicesOperating SystemsComputer Games & Online Games Strategy GuidesHuman-Computer Interaction

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