Bayesian Affect Control Theory

Affect Control Theory is a mathematical representation of the interactions between two persons, in which it is posited that people behave in a way so as to minimize the amount of deflection between their cultural emotional sentiments and the transient emotional sentiments that are created by each situation. Affect Control Theory presents a maximum likelihood solution in which optimal behaviours or identities can be predicted based on past interactions. Here, we formulate a probabilistic and decision theoretic model of the same underlying principles, and show this to be a generalisation of the basic theory. The model is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution. This allows the model to generate affectively believable interactions with people by learning about their identity and predicting their behaviours. We demonstrate this generalisation with a set of simulations. We then show how our model can be used as an emotional "plug-in" for systems that interact with humans. We demonstrate human-interactive capability by building a simple intelligent tutoring application and pilot-testing it in an experiment with 20 participants.