This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Description
Human-agent teams (HATs) are expected to play a larger role in future command and control systems where resilience is critical for team effectiveness. The question of how HATs interact to be effective in both normal and unexpected situations is worthy of further examination. Exploratory behaviors are one that way adaptive

Human-agent teams (HATs) are expected to play a larger role in future command and control systems where resilience is critical for team effectiveness. The question of how HATs interact to be effective in both normal and unexpected situations is worthy of further examination. Exploratory behaviors are one that way adaptive systems discover opportunities to expand and refine their performance. In this study, team interaction exploration is examined in a HAT composed of a human navigator, human photographer, and a synthetic pilot while they perform a remotely-piloted aerial reconnaissance task. Failures in automation and the synthetic pilot’s autonomy were injected throughout ten missions as roadblocks. Teams were clustered by performance into high-, middle-, and low-performing groups. It was hypothesized that high-performing teams would exchange more text-messages containing unique content or sender-recipient combinations than middle- and low-performing teams, and that teams would exchange less unique messages over time. The results indicate that high-performing teams had more unique team interactions than middle-performing teams. Additionally, teams generally had more exploratory team interactions in the first session of missions than the second session. Implications and suggestions for future work are discussed.
ContributorsLematta, Glenn Joseph (Author) / Chiou, Erin K. (Thesis advisor) / Cooke, Nancy J. (Committee member) / Roscoe, Rod D. (Committee member) / Arizona State University (Publisher)
Created2019
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Description
It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI

It is difficult to imagine a society that does not utilize teams. At the same time, teams need to evolve to meet today’s challenges of the ever-increasing proliferation of data and complexity. It may be useful to add artificial intelligent (AI) agents to team up with humans. Then, as AI agents are integrated into the team, the first study asks what roles can AI agents take? The first study investigates this issue by asking whether an AI agent can take the role of a facilitator and in turn, improve planning outcomes by facilitating team processes. Results indicate that the human facilitator was significantly better than the AI facilitator at reducing cognitive biases such as groupthink, anchoring, and information pooling, as well as increasing decision quality and score. Additionally, participants viewed the AI facilitator negatively and ignored its inputs compared to the human facilitator. Yet, participants in the AI Facilitator condition performed significantly better than participants in the No Facilitator condition, illustrating that having an AI facilitator was better than having no facilitator at all. The second study explores whether artificial social intelligence (ASI) agents can take the role of advisors and subsequently improve team processes and mission outcome during a simulated search-and-rescue mission. The results of this study indicate that although ASI advisors can successfully advise teams, they also use a significantly greater number of taskwork interventions than teamwork interventions. Additionally, this study served to identify what the ASI advisors got right compared to the human advisor and vice versa. Implications and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Gutzwiller, Robert S. (Committee member) / Roscoe, Rod D. (Committee member) / Arizona State University (Publisher)
Created2023