Integrating Context into Artificial Intelligence: Research from the Robotics Collaborative Technology Alliance

  • Kristin E. Schaefer US Army Research Laboratory
  • Jean Oh Carnegie Mellon University
  • Derya Aksaray University of Minnesota
  • Daniel Barber University of Central Florida

Abstract

Applying context to a situation, task, or system state provides meaning and advances understanding that can affect future decisions or actions. Although people are naturally good at perceiving contextual understanding and inferring missing pieces of information using various alternative sources, this process is difficult for AI systems or robots, especially in high-uncertainty and unstructured operations. Integration of context-driven AI is important for future robotic capabilities to support the development of situation awareness, calibrate appropriate trust, and improve team performance in collaborative human-robot teams. This article highlights advances in context-driven AI for human-robot teaming by the Army Research Laboratory’s Robotics Collaborative Technology Alliance. Avenues of research discussed include how context enables robots to fill in the gaps to make effective decisions more quickly, supports more robust behaviors, and augments robot communications to suit the needs of the team under a variety of environments and team organizations and across missions.

Published
2019-09-30
How to Cite
Schaefer, K. E., Oh, J., Aksaray, D., & Barber, D. (2019). Integrating Context into Artificial Intelligence: Research from the Robotics Collaborative Technology Alliance. AI Magazine, 40(3), 28-40. https://doi.org/10.1609/aimag.v40i3.2865
Section
Articles