Human-Computer Collaboration in Adaptive Supervisory Control and Function Allocation of Autonomous System Teams

  • Robert S. Gutzwiller
  • Douglas S. Lange
  • John Reeder
  • Rob L. Morris
  • Olinda Rodas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9179)


The foundation for a collaborative, man-machine system for adaptive performance of tasks in a multiple, heterogeneous unmanned system teaming environment is discussed. An autonomics system is proposed to monitor missions and overall system attributes, including those of the operator, autonomy, states of the world, and the mission. These variables are compared within a model of the global system, and strategies that re-allocate tasks can be executed based on a mission-health perspective (such as relieving an overloaded user by taking over incoming tasks). Operators still have control over the allocation via a task manager, which also provides a function allocation interface, and accomplishes an initial attempt at transparency. We plan to learn about configurations of function allocation from human-in-the-loop experiments, using machine learning and operator feedback. Integrating autonomics, machine learning, and operator feedback is expected to improve collaboration, transparency, and human-machine performance.


Autonomics Autonomous systems Supervisory control Task models 



This work was supported by the Space and Naval Warfare Systems Center Pacific Naval Innovative Science and Engineering Program. This work was also supported by the US Department of Defense Autonomy Research Pilot Initiative under the project entitled “Realizing Autonomy via Intelligent Adaptive Hybrid Control”.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Robert S. Gutzwiller
    • 1
  • Douglas S. Lange
    • 1
  • John Reeder
    • 1
  • Rob L. Morris
    • 1
  • Olinda Rodas
    • 1
  1. 1.Space and Naval Warfare Systems Center PacificSan DiegoUSA

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