Accession Number:

AD1076839

Title:

Assessing Artificial Agent Response Time Effects on Human-Agent Teams in Variable Inter-Arrival Time Environments

Descriptive Note:

Technical Report,01 Aug 2017,21 Mar 2019

Corporate Author:

AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON AFB United States

Personal Author(s):

Report Date:

2019-03-21

Pagination or Media Count:

62.0

Abstract:

Autonomous systems have gained an expanded presence within the Department of Defense DoD. Furthermore, the DoD has clearly stated autonomous systems must extend the capabilities of their human operators. Thus, the exploration of strategies for effective pairing of humans and automation supports this vision. Previous research demonstrated that the time at which an automated agent assumes a task for its human teammate, or agent response time ART, affects human-agent team performance, human engagement, and human workload. However, in this research environment, the time between subsequent tasks appearing to the human-agent team, or inter-arrival time IAT, remained constant. Variable IAT environments more accurately reflect real-world operational environments. Previous research also maintained ART at a fixed level. Additionally, the effect of human understanding of automated teammate actions on human-agent team performance remains unknown. This thesis attempts to analyze the effect of an agent with adaptive ART that varies based on current IAT on human-agent team performance, human engagement, and human workload. Additionally, it seeks to determine the implication of agent predictability to the human. This thesis explores these issues in three phases. First, a method and development of a variable ART function for use in future phases is presented. Second, a study of a variable ART teammate against a fixed ART teammate highlights the significance of providing detailed agent instruction to the human. Third, analysis of instruction and type of agent teammate across an entire input IAT function and at different IAT levels is conducted. This work establishes key factors for adaptive ART function implementation. Based on specific IAT changes, the current research demonstrates that adaptive ART can boost human-agent team performance and manipulate human engagement. Furthermore, predictability of agent action in variable IAT environments is a desired system attribute.

Subject Categories:

  • Human Factors Engineering and Man Machine Systems
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE