Fast multi-level adaptation for interactive autonomous characters
ACM Transactions on Graphics (TOG)
Learnable behavioural model for autonomous virtual agents: low-level learning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Memory-based cognitive modeling for robust object extraction and tracking
Applied Intelligence
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Behavioral and cognitive modeling for virtual characters is a promising field. It significantly reduces the workload on the animator, allowing characters to act autonomously in a believable fashion. It also makes interactivity between humans and virtual characters more practical than ever before. In this paper we present a novel technique where an artificial neural network is used to approximate a cognitive model. This allows us to execute the model much more quickly, making cognitively empowered characters more practical for interactive applications. Through this approach, we can animate several thousand intelligent characters in real time on a PC. We also present a novel technique for how a virtual character, instead of using an explicit model supplied by the user, can automatically learn an unknown behavioral/cognitive model by itself through reinforcement learning. The ability to learn without an explicit model appears promising for helping behavioral and cognitive modeling become more broadly accepted and used in the computer graphics community, as it can further reduce the workload on the animator. Further, it provides solutions for problems that cannot easily be modeled explicitly. Copyright © 2004 John Wiley & Sons, Ltd.