Affect Control Processes: Probabilistic and Decision Theoretic Affective Control in Human-Computer Interaction

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 generalization of the basic theory. The new model, called BayesAct, is more expressive than the original theory, as it can maintain multiple hypotheses about behaviours and identities simultaneously as a probability distribution, and can make value-directed action choices. This allows the model to generate affectively believable interactions with people by learning about their identity, predicting their behaviours, and taking actions that are simultaneously goal-directed and affect-sensitive. 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 eliciting knowledge from 37 participants with a survey, and then using this knowledge to build a simple demonstrative intelligent tutoring application. We present results from a pilot study with 20 participants using the application.