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Erschienen in:
Explanation-Based Learning of Action Models Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

2. Automated Domain Model Learning Tools for Planning

verfasst von : Rabia Jilani

Erschienen in: Knowledge Engineering Tools and Techniques for AI Planning

Verlag: Springer International Publishing

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Abstract

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. Domain models can be encoded by human experts or automatically learned through the observation of some existing plans (behaviours). Encoding a domain model manually from experience and intuition is a very complex and time-consuming task, even for domain experts. This chapter investigates various classical and state-of-the-art methods proposed by the researchers to attain the ability of automatic learning of domain models from training data. This concerns with the learning and representation of knowledge about the operator schema, discrete or continuous resources, processes and events involved in the planning domain model. The taxonomy and order of these methods we followed are based on their standing and frequency of usage in the past research. Our intended contribution in this chapter is to provide a broader perspective on the range of techniques in the domain model learning area which underpin the developmental decisions of the learning tools.

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Vorheriges Kapitel Explanation-Based Learning of Action Models
Nächstes Kapitel Formal Knowledge Engineering for Planning: Pre and Post-Design Analysis
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Metadaten
Titel
Automated Domain Model Learning Tools for Planning
verfasst von
Rabia Jilani
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_2