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Explanation-Based Learning of Action Models Read chapter Read first chapter

2020 | OriginalPaper | Chapter

4. MyPDDL: Tools for Efficiently Creating PDDL Domains and Problems

Authors : Volker Strobel, Alexandra Kirsch

Published in: Knowledge Engineering Tools and Techniques for AI Planning

Publisher: Springer International Publishing

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Abstract

The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error- prone. To address this issue, we present myPDDL—a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36% more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48% when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.

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previous chapter Formal Knowledge Engineering for Planning: Pre and Post-Design Analysis
next chapter KEPS Book: Planning.Domains
Appendix
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Footnotes
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Metadata
Title
MyPDDL: Tools for Efficiently Creating PDDL Domains and Problems
Authors
Volker Strobel
Alexandra Kirsch
Copyright Year
2020
Book
Knowledge Engineering Tools and Techniques for AI Planning

Print ISBN: 978-3-030-38560-6

Electronic ISBN: 978-3-030-38561-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-38561-3_4

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