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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