(Chapman & Hall/CRC Artificial Intelligence and Robotics Series) 1st Edition
by Michele Colledanchise (Author), Petter Ögren (Author)
Behavior Trees (BTs) provide a way
to structure the behavior of an artificial agent such as a robot or a
non-player character in a computer game. Traditional design methods,
such as finite state machines, are known to produce brittle behaviors
when complexity increases, making it very hard to add features without
breaking existing functionality. BTs were created to address this very
problem, and enables the creation of systems that are both modular and
reactive. Behavior Trees in Robotics and AI: An Introduction provides
a broad introduction as well as an in-depth exploration of the topic,
and is the first comprehensive book on the use of BTs.
This
book introduces the subject of BTs from simple topics, such as
semantics and design principles, to complex topics, such as learning and
task planning. For each topic, the authors provide a set of examples,
ranging from simple illustrations to realistic complex behaviors, to
enable the reader to successfully combine theory with practice.
Starting
with an introduction to BTs, the book then describes how BTs relate to,
and in many cases, generalize earlier switching structures, or control
architectures. These ideas are then used as a foundation for a set of
efficient and easy to use design principles. The book then presents a
set of important extensions and provides a set of tools for formally
analyzing these extensions using a state space formulation of BTs.
With
the new analysis tools, the book then formalizes the descriptions of
how BTs generalize earlier approaches and shows how BTs can be
automatically generated using planning and learning. The final part of
the book provides an extended set of tools to capture the behavior of
Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success
probabilities and time to completion.
This book targets a
broad audience, including both students and professionals interested in
modeling complex behaviors for robots, game characters, or other AI
agents. Readers can choose at which depth and pace they want to learn
the subject, depending on their needs and background.