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Case-Based Plan Recognition Under Imperfect Observability

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Case-Based Reasoning Research and Development (ICCBR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9343))

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Abstract

SET-PR is a novel case-based recognizer that is robust to three kinds of input errors arising from imperfect observability, namely missing, mislabeled and extraneous actions. We extend our previous work on SET-PR by empirically studying its efficacy on three plan recognition datasets. We found that in the presence of higher input error rates, SET-PR significantly outperforms alternative approaches, which perform similarly to or outperform SET-PR in the presence of no input errors.

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Acknowledgements

Thanks to OSD ASD (R&E) for sponsoring this research. Swaroop Vattam performed this work while an NRC post-doctoral research associate at NRL. Thanks also to the reviewers for their comments. The views and opinions contained in this paper are those of the authors and should not be interpreted as representing the official views or policies of NRL or OSD.

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Authors and Affiliations

  1. NRC Postdoctoral Fellow, Naval Research Laboratory (Code 5514), Washington, DC, USA

    Swaroop S. Vattam

  2. Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory (Code 5514), Washington, DC, USA

    David W. Aha

Authors
  1. Swaroop S. Vattam
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  2. David W. Aha
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Corresponding author

Correspondence to Swaroop S. Vattam .

Editor information

Editors and Affiliations

  1. Universität Paderborn, Paderborn, Germany

    Eyke Hüllermeier

  2. Goethe-Universität Frankfurt, Frankfurt/Main, Germany

    Mirjam Minor

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Vattam, S.S., Aha, D.W. (2015). Case-Based Plan Recognition Under Imperfect Observability. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-24586-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24585-0

  • Online ISBN: 978-3-319-24586-7

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