For training, two humans interact with each other in a somewhat natural way while the system accumulates information about the actions and reactions. This is depicted in detail in Figure 8.1.
Here, the operation is straightforward. The two users are interacting and the learning system is being fed on one end and on the other. Then, once many pairs of data are accumulated, the system uses CEM to optimize a conditioned Gaussian mixture model which represents . Once again, we note the role of the integration symbol which indicates the pre-processing of the past time-series via an attentional window over the past T samples of measurements. This window can be represented compactly in an eigenspace with .
Typically the two users interact for a few minutes indicating to the system some specific patterns of behaviour via a distribution of pairs. Once the CEM converges to a maximum conditional likelihood solution that approximates this distribution, the interaction of the two users has been learned and can be used to generate predictions.