@InProceedings{Sleath2023PossibleModelingErrors,
  author                    = {Kayleigh Sleath and Pascal Bercher},
  title                     = {Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains},
  booktitle                 = {Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023)},
  year                      = {2023},
  publisher                 = {Springer},
  pages                     = {448--454},
  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.},
  doi                       = {10.1007/978-981-99-7022-3_41},
  url_Paper                 = {https://bercher.net/publications/2023/Sleath2023PossibleModelingErrors.pdf},
  url_Slides                = {https://bercher.net/publications/2023/Sleath2023PossibleModelingErrorsSlides.pdf},
  url_video_of_presentation = {https://www.youtube.com/watch?v=5TR7Hf9rlpI},
  url_zenodo                = {https://zenodo.org/records/8249690},
  keywords                  = {conference}
}

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