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Characterization of Indicators for Adaptive Human-Swarm Teaming.

Aya Hussein1, Leo Ghignone1, Tung Nguyen1

  • 1School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.

Frontiers in Robotics and AI
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

This study explores adaptive autonomy for human-swarm interaction, proposing the MICAH framework to integrate human and swarm agents effectively for complex tasks.

Keywords:
adaptive autonomyautomation indicatorscomplexity indicatorshuman cognitive state assessmenthuman-swarm interactioninteraction indicatorsmission performance indicators

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Area of Science:

  • Robotics and Artificial Intelligence
  • Human-Computer Interaction
  • Multi-Agent Systems

Background:

  • Swarm systems offer autonomous collaboration for diverse applications like search and rescue and cyber defense.
  • Effective human-swarm teaming is crucial for successful deployment, requiring seamless integration.
  • Adaptive autonomy enhances human-machine interaction but needs specific considerations for human-swarm contexts.

Purpose of the Study:

  • To review multidisciplinary literature on facilitating adaptive autonomy in human-swarm interaction.
  • To identify and discuss key factors necessary for adaptive agent operation.
  • To propose a framework for mapping state indicators essential for adaptive human-swarm teaming.

Main Methods:

  • Literature review of multidisciplinary research on adaptive autonomy in human-swarm interaction.
  • Identification of five critical aspects for adaptive agent operation: mission objectives, interaction, mission complexity, automation levels, and human states.
  • Distillation of corresponding indicators within each aspect.

Main Results:

  • Five key aspects influencing adaptive autonomy in human-swarm interaction were identified and discussed.
  • Primitive state indicators for each aspect were distilled.
  • A framework named MICAH (Mission-Interaction-Complexity-Automation-Human) was proposed.

Conclusions:

  • The MICAH framework provides a structured approach to understanding and implementing adaptive autonomy in human-swarm systems.
  • Effective human-swarm teaming relies on carefully considering mission objectives, interaction dynamics, complexity, automation, and human states.
  • This research contributes to advancing the capabilities of autonomous swarms through enhanced human-machine integration.