We propose Action-Reaction Learning as an approach for analyzing and synthesizing human behaviour. This paradigm uncovers causal mappings between past and future events or between an action and its reaction by observing time sequences. We apply this method to analyze human interaction and to subsequently synthesize human behaviour. Using a time series of perceptual measurements, a system automatically discovers correlations between past gestures from one human participant (action) and a subsequent gesture (reaction) from another participant. A probabilistic model is trained from data of the human interaction using a novel estimation technique, Conditional Expectation Maximization (CEM). The estimation uses general bounding and maximization to monotonically find the maximum conditional likelihood solution. The learning system drives a graphical interactive character which probabilistically predicts a likely response to a user's behaviour and performs it interactively. Thus, after analyzing human interaction in a pair of participants, the system is able to replace one of them and interact with a single remaining user.
Keywords: interaction learning, behavior, conditional likelihood
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