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Explanation-Based Learning of Action Models

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Knowledge Engineering Tools and Techniques for AI Planning

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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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Acknowledgements

This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jiménez by the RYC15/18009, both programs funded by the Spanish government.

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Authors and Affiliations

  1. Departamento de Sistemas Informáticos y Computación, Universitat Politècnica de València, Valencia, Spain

    Diego Aineto, Sergio Jiménez & Eva Onaindia

Authors
  1. Diego Aineto
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  2. Sergio Jiménez
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  3. Eva Onaindia
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Corresponding author

Correspondence to Sergio Jiménez .

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Editors and Affiliations

  1. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  2. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Diane Kitchin

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Aineto, D., Jiménez, S., Onaindia, E. (2020). Explanation-Based Learning of Action Models. In: Vallati, M., Kitchin, D. (eds) Knowledge Engineering Tools and Techniques for AI Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-38561-3_1

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38560-6

  • Online ISBN: 978-3-030-38561-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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