Search Results - "Wray, Kyle H"

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  1. 1

    Improving Competence via Iterative State Space Refinement by Basich, Connor, Svegliato, Justin, Beach, Allyson, Wray, Kyle H., Witwicki, Stefan, Zilberstein, Shlomo

    “…Despite considerable efforts by human designers, accounting for every unique situation that an autonomous robotic system deployed in the real world could face…”
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    Conference Proceeding
  2. 2

    Experience Filter: Using Past Experiences on Unseen Tasks or Environments by Yildiz, Anil, Yel, Esen, Corso, Anthony L., Wray, Kyle H., Witwicki, Stefan J., Kochenderfer, Mykel J.

    “…One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen…”
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    Conference Proceeding
  3. 3

    Competence-aware systems by Basich, Connor, Svegliato, Justin, Wray, Kyle H., Witwicki, Stefan, Biswas, Joydeep, Zilberstein, Shlomo

    Published in Artificial intelligence (01-03-2023)
    “…Building autonomous systems for deployment in the open world has been a longstanding objective in both artificial intelligence and robotics. The open world,…”
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    Journal Article
  4. 4
  5. 5

    Semi-Markovian Planning to Coordinate Aerial and Maritime Medical Evacuation Platforms by Al-Husseini, Mahdi, Wray, Kyle H, Kochenderfer, Mykel J

    Published 06-10-2024
    “…The transfer of patients between two aircraft using an underway watercraft increases medical evacuation reach and flexibility in maritime environments. The…”
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    Journal Article
  6. 6

    Watercraft as Overwater Ambulance Exchange Points to Enhance Aeromedical Evacuation by Al-Husseini, Mahdi, Wray, Kyle H, Kochenderfer, Mykel J

    Published 25-08-2024
    “…Ambulance exchange points are preidentified sites where patients are transferred between evacuation platforms while en route to enhanced medical care. We…”
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    Journal Article
  7. 7

    Rao-Blackwellized POMDP Planning by Lee, Jiho, Ahmed, Nisar R, Wray, Kyle H, Sunberg, Zachary N

    Published 24-09-2024
    “…Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires…”
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    Journal Article
  8. 8

    Constrained Hierarchical Monte Carlo Belief-State Planning by Jamgochian, Arec, Buurmeijer, Hugo, Wray, Kyle H., Corso, Anthony, Kochenderfer, Mykel J.

    “…Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints,…”
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    Conference Proceeding
  9. 9

    Constrained Hierarchical Monte Carlo Belief-State Planning by Jamgochian, Arec, Buurmeijer, Hugo, Wray, Kyle H, Corso, Anthony, Kochenderfer, Mykel J

    Published 30-10-2023
    “…Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints,…”
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    Journal Article
  10. 10

    Experience Filter: Using Past Experiences on Unseen Tasks or Environments by Yildiz, Anil, Yel, Esen, Corso, Anthony L, Wray, Kyle H, Witwicki, Stefan J, Kochenderfer, Mykel J

    Published 29-05-2023
    “…One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen…”
    Get full text
    Journal Article