https://arxiv.org/abs/1709.00084

close this message

Donate to arXiv

Please join the Simons Foundation and our generous member
organizations in supporting arXiv during our giving campaign
September 23-27. 100% of your contribution will fund improvements and
new initiatives to benefit arXiv's global scientific community.

DONATE

[secure site, no need to create account]

Skip to main content
Cornell University
We gratefully acknowledge support from
the Simons Foundation and member institutions.
 
arXiv.org > cs > arXiv:1709.00084
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Robotics

arXiv:1709.00084 (cs)
[Submitted on 31 Aug 2017 (v1), last revised 3 Jun 2020 (this
version, v4)]

Title:Behavior Trees in Robotics and AI: An Introduction

Authors:Michele Colledanchise, Petter Ogren
Download PDF

    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.

Subjects:          Robotics (cs.RO); Artificial Intelligence (cs.AI)
Journal reference: Chapman & Hall/CRC Artificial Intelligence and
                   Robotics Series 2018
DOI:               10.1201/9780429489105
Report number:     ISBN 9781138593732
Cite as:           arXiv:1709.00084 [cs.RO]
                   (or arXiv:1709.00084v4 [cs.RO] for this version)

Submission history

From: Michele Colledanchise [view email]
[v1] Thu, 31 Aug 2017 21:05:18 UTC (7,737 KB)
[v2] Thu, 21 Sep 2017 07:33:32 UTC (7,737 KB)
[v3] Mon, 15 Jan 2018 17:41:24 UTC (14,548 KB)
[v4] Wed, 3 Jun 2020 16:25:31 UTC (14,548 KB)
Full-text links:

Download:

  * PDF
  * Other formats

(license)
Current browse context:
cs.RO
< prev   |   next >
new | recent | 1709
Change to browse by:
cs
cs.AI

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

DBLP - CS Bibliography

listing | bibtex
Michele Colledanchise
Petter Ogren
a export bibtex citation Loading...

Bibtex formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
( ) Code

Code Associated with this Article

[ ] arXiv Links to Code Toggle
arXiv Links to Code (What is Links to Code?)
( ) Related Papers

Recommenders and Search Tools

[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs and how to get involved.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack