Motivated Reinforcement Learning
Curious Characters for Multiuser Games
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Beschreibung
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.
Motivated reinforcement learning agents are applied as a novel approach to designing dynamic, adaptive characters for multiuser, online games Includes supplementary material: sn.pub/extras
Autor*in
Kathryn E. Merrick
Themen in »Motivated Reinforcement Learning«
Agents Artificial life Cognition Computer games Design Games Learning Motivated learning Multiuser games Online games Second Life artificial intelligence intelligence machine learning modeling