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

  • Bryan AuslanderEmail author
  • Michael W. Floyd
  • Thomas Apker
  • Benjamin Johnson
  • Mark Roberts
  • David W. Aha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9343)


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.


Autonomous Vehicle Problem Feature High Fidelity Simulator Mission Manager Goal Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bryan Auslander
    • 1
    Email author
  • Michael W. Floyd
    • 1
  • Thomas Apker
    • 2
  • Benjamin Johnson
    • 3
  • Mark Roberts
    • 3
  • David W. Aha
    • 2
  1. 1.Knexus Research CorporationSpringfieldUSA
  2. 2.Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory (Code 5514)Washington, DCUSA
  3. 3.NRC Postdoctoral Fellow, Naval Research Laboratory (Code 5514)Washington, DCUSA

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