Predicting User Psychological Characteristics from Interactions with Empathetic Virtual Agents

Authors:
Jennifer Robison;Jonathan Rowe;Scott Mcquiggan;James Lester
Affiliations:
Department of Computer Science, North Carolina State University, Raleigh;Department of Computer Science, North Carolina State University, Raleigh;SAS Institute, Cary;Department of Computer Science, North Carolina State University, Raleigh
Venue:
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Year:
2009

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Abstract

Enabling virtual agents to quickly and accurately infer users' psychological characteristics such as their personality could support a broad range of applications in education, training, and entertainment. With a focus on narrative-centered learning environments, this paper presents an inductive framework for inferring users' psychological characteristics from observations of their interactions with virtual agents. Trained on traces of users' interactions with virtual agents in the environment, psychological user models are induced from the interactions to accurately infer different aspects of a user's personality. Further, analyses of timing data suggest that these induced models are also able to converge on correct predictions after a relatively small number of interactions with virtual agents.