Motivated Reinforcement Learning: Curious Characters for Multiuser Games

Description

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. Front Matter....Pages i-xiv Front Matter....Pages 1-1 Non-Player Characters in Multiuser Games....Pages 3-16 Motivation in Natural and Artificial Agents....Pages 17-43 Towards Motivated Reinforcement Learning....Pages 45-70 Comparing the Behaviour of Learning Agents....Pages 71-88 Front Matter....Pages 89-89 Curiosity, Motivation and Attention Focus....Pages 91-120 Motivated Reinforcement Learning Agents....Pages 121-134 Front Matter....Pages 135-135 Curious Characters for Multiuser Games....Pages 137-149 Curious Characters for Games in Complex, Dynamic Environments....Pages 151-170 Curious Characters for Games in Second Life ....Pages 171-189 Front Matter....Pages 191-191 Towards the Future....Pages 193-199 Back Matter....Pages 201-206