Motivation driven learning for interactive synthetic characters

Abstract

Adaptation capability and a transparent motivation system greatly aid real time interactions between humans and synthetic characters. These components enhance the life-like impression that the characters make, and enable comfortable communication between the characters and human participants. We extended the behavioral action selection system of Blumberg and Kline with these needs in mind, and developed a creature kernel that enables the designing of a character with communicative motivational and emotional states, and learning abilities based on feedback from the motivation system. In this paper, we introduce this new approach to character design, and how various learning algorithms have been incorporated within this framework. The main characters for an interactive installation, (void*): A cast of characters, have been created using this developed creature kernel. We describe results with examples of alteration of attitudes, learning of concepts, and formation of emotional reactions to locations based on experience.