The Effect of Information Level on Human-Agent Interaction for Route Planning

Abstract

This study investigated how differing levels of information affected decisions human operators made in a route-selection task. Experiment 1 examined how information about resource usage/requirements affected route-selection decisions for a remotely based supervisor guiding a dismounted Soldier unit through an urban environment. Experiment 2 included all the information from Experiment 1 but added a robotic asset and its resource usage/requirements information. Results suggest that a low level of information corresponds to a significantly lower decision time (DT) than its counterparts, and DT at all information levels increased with the addition of the robotic asset. Participants exhibited sensemaking by reducing DTs as the experiment progressed. In addition, as the amount of information increased, preference for specific information sources began to vary. In the condition with the greatest amount of information available, participants displayed no clear consensus as to preferred information source, with many indicating they preferred sources that were unsuitable for successful mission completion. Future research could investigate further into the complexity of appropriate display for user interfaces involving robotic assets.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2015
Accession Number
AD1039795

Entities

People

  • Jessie Y. Chen
  • Julia L Wright
  • Michael J. Barnes
  • Michael W. Boyce

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Computer Graphics
  • Computers
  • Control Systems
  • Data Displays
  • Environment
  • Graphics
  • Human Factors Engineering
  • Human Systems Integration
  • Human-Robot Interaction
  • Identification Systems
  • Image Processing
  • Information Processing
  • Information Science
  • Psychology
  • Training
  • Unmanned Systems
  • User Interface

Readers

  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • Autonomy
  • Autonomy - Human-Robot Interaction