An Experimental Investigation of how Robotic Learning and Cloud-Based Information Affects Trust in Human-Machine Teaming Contexts

Abstract

The research goals of the current proposal are to investigate two factors that may influence trust in future autonomous systems. The first factor is machine learning. Many intelligent machines are designed to learn from their environment in order to improve performance and reduce error over time. This is an important capability for adaptive autonomous system, but potentially effects human-machine teaming in unintended ways. Predictability tends to have a significant effect on a human operator s trust and willingness to rely on autonomous systems, but learning machines that will adapt their behavior over time, and will therefore be less predictable than the non-learning systems that people typically interact with.

Document Details

Document Type
DoD Grant Award
Publication Date
Jul 15, 2016
Source ID
FA95501610217

Entities

People

  • Nhut Ho

Organizations

  • Air Force Office of Scientific Research
  • United States Air Force

Tags

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Human-Robot Interaction