Multiple Behavior Models

Human behavior, in all but the simplest tasks, is not as simple as a single dynamic model. The next most complex model of human behavior is to have several alternative models of the person's dynamics, one for each class of response. Then at each instant we can make observations of the person's state, decide which model applies, and then use that model for estimation. This is known as the multiple model or generalized likelihood approach, and produces a generalized maximum likelihood estimate of the current and future values of the state variables [22]. Moreover, the cost of the Kalman filter calculations is sufficiently small to make the approach quite practical.

Intuitively, this solution breaks the person's overall behavior down into several ``prototypical'' behaviors. For instance, we might have dynamic models corresponding to a relaxed state, a very stiff state, and so forth. We then classify the behavior by determining which model best fits the observations. This Is similar to the multiple model approach of Friedmann 1993, and Isard 1996[7,11].

Since the innovations process is the part of the observation data that is unexplained by the dynamic model, the behavior model that explains the largest portion of the observations is, of course, the model most likely to be correct. Thus, at each time step, we calculate the probability Pr⁽ⁱ⁾ of the m-dimensional observations ${\bf Y}_k$ given the i^th model and choose the model with the largest probability. This model is then used to estimate the current value of the state variables, to predict their future values, and to choose among alternative responses.