Presentation
42. How Can Artificial Intelligence Team-Mates Know What Humans Want? Using Eye-Tracking Data to Infer Human Preferences in Game-Theoretic Decision Tasks
SessionPoster Session 2
DescriptionFor human-agent teams, it is as important for agents to have models of their human teammates as it is for humans to have models of their agent teammates. However, approaches to knowledge elicitation have wrestled with the problem of capturing human knowledge when much of it is tacit and difficult to verbalize. By observing the choices that people make under different conditions we can infer the choice structure they appear to be following. In this way, observations could allow the agent to predict its human team-mates’ choices and actions. We report an experiment in which eye-tracking is used to capture choices in a constrained task and then develop a decision model that can replicate aspects of these choices. We propose that such a model can support human-agent teams by enabling inferences of some aspects of human decision-making.
Event Type
Poster
TimeThursday, September 12th5:30pm - 6:30pm MST
LocationMcArthur Ballroom
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