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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.
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.
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