Connectionist modelling of skill dynamics

This paper discusses a method for modelling skilled action for synthetic actors in a virtual environment. The method guides lower-level motor skills from a connectionist model of skill memory, implemented as collections of trained neural networks. The relationship between this work and that of other projects in task-level animation is discussed, the principles of connectionist learning are explained, and a series of experiments testing the concept of connectionist skill modelling are reviewed.

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