High Assurance Human-Centric Decision Systems

Abstract

Many future decision support systems will be human-centric, i.e., require substantial human oversight and control. Because these systems often provide critical services, high assurance will be needed that they satisfy their requirements. How to develop high assurance human-centric decision systems is unknown: while significant research has been conducted in areas such as agents, cognitive science, and formal methods, how to apply and integrate the design principles and disparate models in each area is unclear. This paper proposes a novel process for developing human-centric decision systems where AI (artificial intelligence) methods namely, cognitive models to predict human behavior and agents to assist the human are used to achieve adequate system performance, and software engineering methods, namely, formal modeling and analysis, to obtain high assurance. To support this process, the paper introduces a software engineering technique formal model synthesis from scenarios and two AI techniques a model for predicting human overload and user model synthesis from participant studies data. To illustrate the process and techniques, the paper describes a decision system controlling unmanned air vehicles.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2013
Accession Number
ADA599779

Entities

People

  • Constance Heitmeyer
  • David Aha
  • Elizabeth Leonard
  • J. Gregory Trafton
  • Len Breslow
  • Marc Pickett

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence
  • Autonomous Vehicles
  • Case Studies
  • Cognitive Science
  • Cognitive Systems Engineering
  • Complex Systems
  • Computer Programs
  • Engineering
  • Human Behavior
  • Machine Learning
  • Overload
  • Software Development
  • Supervisory Control
  • Unmanned Aerial Vehicles
  • Unmanned Vehicles
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Software Engineering.

Technology Areas

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