Abstract
We introduce Iterative Perceptual Learning (IPL), a novel approach to learn computational models for social behavior synthesis from corpora of human-human interactions. IPL combines perceptual evaluation with iterative model refinement. Human observers rate the appropriateness of synthesized behaviors in the context of a conversation. These ratings are used to refine
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