Characterization of Indicators for Adaptive Human-Swarm Teaming

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.