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Learning to Estimate: A Case-Based Approach to Task Execution Prediction

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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

A system that controls a team of autonomous vehicles should be able to accurately predict the expected outcomes of various subtasks. For example, this may involve estimating how well a vehicle will perform when searching a designated area. We present CBE, a case-based estimation algorithm, and apply it to the task of predicting the performance of autonomous vehicles using simulators of varying fidelity and past performance. Since there are costs to evaluating the performance in simulators (i.e., higher fidelity simulators are more computationally expensive) and in deployment (i.e., potential human injury and deployment expenses), CBE uses a variant of local linear regression to estimate values that cannot be directly evaluated, and incrementally revises its case base. We empirically evaluate CBE on Humanitarian Assistance/Disaster Relief (HA/DR) scenarios and show it to be more accurate than several baselines and more efficient than using a low fidelity simulator.

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Acknowledgements

Thanks to OSD ASD (R&E) for sponsoring this research.

Author information

Authors and Affiliations

  1. Knexus Research Corporation, Springfield, VA, USA

    Bryan Auslander & Michael W. Floyd

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

    Thomas Apker & David W. Aha

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

    Benjamin Johnson & Mark Roberts

Authors
  1. Bryan Auslander
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  2. Michael W. Floyd
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  3. Thomas Apker
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  4. Benjamin Johnson
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  5. Mark Roberts
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  6. David W. Aha
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Corresponding author

Correspondence to Bryan Auslander .

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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Auslander, B., Floyd, M.W., Apker, T., Johnson, B., Roberts, M., Aha, D.W. (2015). Learning to Estimate: A Case-Based Approach to Task Execution Prediction. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-24586-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24585-0

  • Online ISBN: 978-3-319-24586-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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