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.
