Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Empirically building and evaluating a probabilistic model of user affect

Empirically building and evaluating a probabilistic model of user affect We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users’ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png User Modeling and User-Adapted Interaction Springer Journals

Empirically building and evaluating a probabilistic model of user affect

Loading next page...
 
/lp/springer-journals/empirically-building-and-evaluating-a-probabilistic-model-of-user-0HRq0qvAYd

References (97)

Publisher
Springer Journals
Copyright
Copyright © 2009 by Springer Science+Business Media B.V.
Subject
Computer Science; User Interfaces and Human Computer Interaction; Multimedia Information Systems; Management of Computing and Information Systems
ISSN
0924-1868
eISSN
1573-1391
DOI
10.1007/s11257-009-9062-8
Publisher site
See Article on Publisher Site

Abstract

We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users’ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.

Journal

User Modeling and User-Adapted InteractionSpringer Journals

Published: Jan 30, 2009

There are no references for this article.