Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.
College and Department
Physical and Mathematical Sciences; Computer Science
BYU ScholarsArchive Citation
Dinerstein, Jonathan J., "Improving and Extending Behavioral Animation Through Machine Learning" (2005). All Theses and Dissertations. 310.
computer animation, behavioral animation, character animation, synthetic characters, behavioral modeling, cognitive modeling, machine learning, reinforcement learning, programming by demonstration, autonomous agents, AI-based animation, computer games, training simulators