Date of Award
3-23-2017
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Systems Engineering and Management
First Advisor
Michael E. Miller, PhD.
Abstract
It is often useful to understand the impact of an artificial teammate upon human workload in human-machine teams. Levels of Autonomy (LoA) differentiate systems based on control authority. Unfortunately, human workload is not necessarily correlated with LoA. An alternate classification framework, designated the Level of Human Control Abstraction (LHCA), is proposed. LHCA differentiates system states based on the control and monitoring tasks performed and the level of decisions made by humans. The framework defines five levels, designed to differentiate between system states based upon anticipated levels of human attention. This presentation will summarize the framework and demonstrate its application.
AFIT Designator
AFIT-ENV-MS-17-197
DTIC Accession Number
AD1055236
Recommended Citation
Johnson, Clifford D., "A Framework for Analyzing and Discussing Level of Human Control Abstraction" (2017). Theses and Dissertations. 1656.
https://scholar.afit.edu/etd/1656