An Interdependent Model of Personality, Motivation, Emotion, and Mood for Intelligent Virtual Agents

Abstract

Building intelligent agents that can believably interact with humans is a difficult yet important task in a host of applications, including therapy, education, and entertainment. We submit that in order to enhance believability, the agent's affective state should be accurately modeled and should realistically influence the agent's behavior. We propose a computational model of affect which incorporates an empirically-based interplay between its various affective components - personality, motivation, emotion, and mood. Further, our model captures a number of salient mechanisms that are observable in humans and that influence the agent's behavior. We are therefore hopeful that our model will facilitate more engaging and meaningful human-agent interactions. We evaluate our model and illustrate its efficacy, as well as the importance of the different components in the model and their interplay.