Global ETD Search

1	Improving and Extending Behavioral Animation Through Machine Learning Dinerstein, Jonathan J. 20 April 2005 (has links) (PDF) 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. 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
2	Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters Whiting, Jeffrey S. 08 June 2007 (has links) (PDF) Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's performance, and to create new behaviors and animations. This thesis provides a framework that can aggregate together existing behavioral and cognitive models into an ensemble. An animator only has to rate how appropriately a character performed and through machine learning the system is able to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach taken. cognitive modeling behavioral modeling ensembles machine learning ensembles cognitive model ensembles behavioral model ensembles computer animation behavioral animation character animation synthetic characters behavioral modeling machine learning autonomous agents AI-based animation computer games Computer Sciences

Search results

Improving and Extending Behavioral Animation Through Machine Learning

Cognitive and Behavioral Model Ensembles for Autonomous Virtual Characters