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Detecting AI Planning Modelling Mistakes – Potential Errors and Benchmark Domains

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14326))

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Abstract

AI planning systems can solve complex problems, leaving domain creation as one of the largest obstacles to a large-scale application of this technology. Domain modeling is a tedious, error-prone and manual process. Unfortunately, domain modelling assistance software is sparse and mostly restricted to editors with only surface-level functionality such as syntax highlighting. We address this important gap by proposing a list of potential domain errors which can be detected by problem parsers and modeling tools. We test well-known planning systems and modeling editors on models with those errors and report their results.

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Notes

  1. 1.

    https://github.com/ProfDrChaos/flawedPlanningModels.

References

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Acknowledgements

We would like to thank Bernd Schattenberg for discussions (and one of the reported errors).

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Authors and Affiliations

  1. School of Computing, The Australian National University, Canberra, Australia

    Kayleigh Sleath & Pascal Bercher

Authors
  1. Kayleigh Sleath
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  2. Pascal Bercher
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Corresponding author

Correspondence to Pascal Bercher .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS (Malaysia), Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sleath, K., Bercher, P. (2024). Detecting AI Planning Modelling Mistakes – Potential Errors and Benchmark Domains. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14326. Springer, Singapore. https://doi.org/10.1007/978-981-99-7022-3_41

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  • DOI: https://doi.org/10.1007/978-981-99-7022-3_41

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7021-6

  • Online ISBN: 978-981-99-7022-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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