Behavior Trees in Robotics and AI: An Introduction

TL;DR

This paper introduces Behavior Trees as a modular, reactive structure for autonomous agents, detailing their properties, analysis tools, and applications in planning and machine learning, with extensions for stochastic behaviors.

Contribution

It provides a comprehensive introduction to BTs, formal analysis methods, and extensions for stochastic behaviors, advancing their application in AI and robotics.

Findings

BTs are efficient, modular, and reactive task-switching structures.

Formal tools enable analysis of safety, robustness, and efficiency.

Extensions allow modeling success probabilities and time to completion.

Abstract

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe 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.