Improving and Extending Behavioral Animation Through Machine Learning

<p><em>Behavioral animation</em> 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., <em>cognitive models</em>) 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.</p>

Name thesis:degree_name

PhD

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Dinerstein, Jonathan J.

• 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
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/310

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1309