Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Reliance on computer-mediated teaming has exploded in recent years, making research on how teammates calibrate their behavior critical. Here, we offer a simplistic, viable method to model human behavior for use in subsequent research investigating coordination among partners. We collected human performance data in a multiple object tracking task and a communications task to serve as the basis of our agent performance in multiple tasks. We demonstrate our model in real-time by drawing from existing research involving probabilistic models of detecting critical events and sample from a parametric log normal model of human response times to mimic human behavior. We endow our agent with team-based etiquette through a hesitancy to intervene, a parameter sampled from a uniform distribution, and manipulated agent performance through parametric shifts to detection and the log normal distribution that represents agent response times. The present work does not offer hypotheses as we did not conduct an experiment. Rather, we derive and provide a validation of an agent modeled from human performance parameters in two tasks for future team-level research with ad hoc partners.
Recommended Citation
Fox, Elizabeth; Bowers, Gregory; Capiola, August; and Stephenson, Arielle, "The Design of an Ostensible Human Teammate" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/cl/human-ai_collaboration/2
The Design of an Ostensible Human Teammate
Online
Reliance on computer-mediated teaming has exploded in recent years, making research on how teammates calibrate their behavior critical. Here, we offer a simplistic, viable method to model human behavior for use in subsequent research investigating coordination among partners. We collected human performance data in a multiple object tracking task and a communications task to serve as the basis of our agent performance in multiple tasks. We demonstrate our model in real-time by drawing from existing research involving probabilistic models of detecting critical events and sample from a parametric log normal model of human response times to mimic human behavior. We endow our agent with team-based etiquette through a hesitancy to intervene, a parameter sampled from a uniform distribution, and manipulated agent performance through parametric shifts to detection and the log normal distribution that represents agent response times. The present work does not offer hypotheses as we did not conduct an experiment. Rather, we derive and provide a validation of an agent modeled from human performance parameters in two tasks for future team-level research with ad hoc partners.
https://aisel.aisnet.org/hicss-56/cl/human-ai_collaboration/2