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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

1. Explanation-Based Learning of Action Models

verfasst von : Diego Aineto, Sergio Jiménez, Eva Onaindia

Erschienen in: Knowledge Engineering Tools and Techniques for AI Planning

Verlag: Springer International Publishing

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Abstract

The paper presents a classical planning compilation for learning STRIPS action models from partial observations of plan executions. The compilation is flexible to different amounts and types of input knowledge, from learning samples that comprise partially observed intermediate states of the plan execution to samples in which only the initial and final states are observed. The compilation accepts also partially specified action models and it can be used to validate whether an observation of a plan execution follows a given STRIPS action model, even if the given model or the given observation is incomplete.

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Nächstes Kapitel Automated Domain Model Learning Tools for Planning
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Metadaten
Titel
Explanation-Based Learning of Action Models
verfasst von
Diego Aineto
Sergio Jiménez
Eva Onaindia
Copyright-Jahr
2020
Buch
Knowledge Engineering Tools and Techniques for AI Planning

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

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

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-38561-3_1