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MyPDDL: Tools for Efficiently Creating PDDL Domains and Problems

par Strobel, Volker ;Kirsch, Alexandra
Editeur scientifique Vallati, Mauro;Kitchin, Diane
Référence Knowledge Engineering Tools and Techniques for AI Planning, Springer, Cham, page (67-90)
Publication Publié, 2020-03-26

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Résumé :

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.