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Description
Although current urban search and rescue (USAR) robots are little more than remotely controlled cameras, the end goal is for them to work alongside humans as trusted teammates. Natural language communications and performance data are collected as a team of humans works to carry out a simulated search and rescue

Although current urban search and rescue (USAR) robots are little more than remotely controlled cameras, the end goal is for them to work alongside humans as trusted teammates. Natural language communications and performance data are collected as a team of humans works to carry out a simulated search and rescue task in an uncertain virtual environment. Conditions are tested emulating a remotely controlled robot versus an intelligent one. Differences in performance, situation awareness, trust, workload, and communications are measured. The Intelligent robot condition resulted in higher levels of performance and operator situation awareness (SA).
ContributorsBartlett, Cade Earl (Author) / Cooke, Nancy J. (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Wu, Bing (Committee member) / Arizona State University (Publisher)
Created2015
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Description
This research evaluates a cyber test-bed, DEXTAR (Defense Exercises for Team Awareness Research), and examines the relationship between good and bad team performance in increasingly difficult scenarios. Twenty-one computer science graduate students (seven three-person teams), with experience in cybersecurity, participated in a team-based cyber defense exercise in the context of

This research evaluates a cyber test-bed, DEXTAR (Defense Exercises for Team Awareness Research), and examines the relationship between good and bad team performance in increasingly difficult scenarios. Twenty-one computer science graduate students (seven three-person teams), with experience in cybersecurity, participated in a team-based cyber defense exercise in the context of DEXTAR, a high fidelity cybersecurity testbed. Performance measures were analyzed in addition to team process, team behavior, and workload to examine the relationship between good and bad teams. Lessons learned are reported that will inform the next generation of DEXTAR.
ContributorsBradbury, Aaron (Author) / Cooke, Nancy J. (Thesis advisor) / Branaghan, Russell (Committee member) / Roscoe, Rod (Committee member) / Arizona State University (Publisher)
Created2016
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Description
Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to

Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed.
ContributorsBuchanan, Verica (Author) / Cooke, Nancy J. (Thesis advisor) / Maciejewski, Ross (Committee member) / Craig, Scotty D. (Committee member) / Arizona State University (Publisher)
Created2016
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Description
The goal of this experiment is to observe the relation between synchrony and performance in 3-person teams in a simulated Army medic training environment (i.e., Monitoring Extracting and Decoding Indicators of Cognitive workload: MEDIC). The cardiac measure Interbeat-Interval (IBI) was monitored during a physically oriented, and a cognitively oriented

The goal of this experiment is to observe the relation between synchrony and performance in 3-person teams in a simulated Army medic training environment (i.e., Monitoring Extracting and Decoding Indicators of Cognitive workload: MEDIC). The cardiac measure Interbeat-Interval (IBI) was monitored during a physically oriented, and a cognitively oriented task. IBI was measured using NIRS (Near-Infrared Spectrology), and performance was measured using a team task score during a balance board and puzzle task. Synchrony has not previously been monitored across completely different tasks in the same experiment. I hypothesize that teams with high synchrony will show high performance on both tasks. Although no significant results were discovered by the correlational analysis, a trend was revealed that suggests there is a positive relationship between synchrony and performance. This study has contributed to the literature by monitoring physiological measures in a simulated team training environment, making suggestions for future research.
ContributorsFedele, Michael A (Author) / Cooke, Nancy J. (Thesis advisor) / Gray, Rob (Committee member) / Roscoe, Rod (Committee member) / Arizona State University (Publisher)
Created2016