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[2] and Kline[14] 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 rea...

Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

Inspired by recent work in ethology and animal training, we integrate representations for time and rate into a behavior-based architecture for autonomous virtual creatures. The resulting computational model of affect and action selection allows these creatures to discover and refine their understanding of apparent temporal causality relationships which may or may not involve self-action. The fundamental action selection choice that a creature must make in order to satisfy its internal needs is whether to explore, react or exploit. In this architecture, that choice is informed by an understanding of apparent temporal causality, the representation for which is integrated into the representation for action. The ability to accommodate changing ideas about causality allows the creature to exist in and adapt to a dynamic world. Not only is such a model suitable for computational systems, but its derivation from biological models suggests that it may also be useful for gaining a new perspective on learning in biological systems. The implementation of a complete character built using this architecture is able to reproduce a variety of conditioning phenomena, as well as learn using a training technique used with live animals.