Acquiring User Models to Test Automated Assistants

Abstract

A central problem in decision support tasks is operator overload in which a human operator's performance suffers because he or she is overwhelmed by the cognitive requirements of a task. To alleviate this problem, it would be useful to provide the human operator with an automated assistant to share some of the task's cognitive load. However, the development cycle for building an automated assistant is hampered by the testing phase because this involves human user studies which are costly and time-consuming to conduct. As an alternative to user studies, we propose acquiring user models which can be used as a proxy for human users during middle iterations, thereby significantly shortening the development cycle for rapid development. The primary contribution of this paper is a method for coarsely testing automated assistants by using user models acquired from traces gathered from various individual human operators. We apply this method in a case study in which we evaluate an automated assistant for users operating in a simulation of multiple unmanned aerial vehicles.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2013
Accession Number
ADA576883

Entities

People

  • David W. Aha
  • J. Gregory Trafton
  • Marc Pickett

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Case Studies
  • Flight Paths
  • Information Science
  • Machine Learning
  • Reaction Time
  • Simulations
  • Simulators
  • Supervised Machine Learning
  • Test Sets
  • Unmanned
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Systems Analysis and Design

Technology Areas

  • Autonomy
  • Autonomy - Human-Robot Interaction