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ICVS
1999
Springer

Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour

9 years 5 months ago
Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour
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 uncovers correlations between past gestures from one human participant (an action) and a subsequent gesture (a 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 nd 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, afte...
Tony Jebara, Alex Pentland
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where ICVS
Authors Tony Jebara, Alex Pentland
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