  This paper describes a novel method for building affectively intelligent
human-interactive agents. The method is based on a key sociological insight
that has been developed and extensively verified over the last twenty years,
but has yet to make an impact in artificial intelligence. The insight is that
resource bounded humans will, by default, act to maintain affective
consistency. Humans have culturally shared fundamental affective sentiments
about identities, behaviours, and objects, and they act so that the transient
affective sentiments created during interactions confirm the fundamental
sentiments. Humans seek and create situations that confirm or are consistent
with, and avoid and supress situations that disconfirm or are inconsistent
with, their culturally shared affective sentiments. This "affect control
principle" has been shown to be a powerful predictor of human behaviour. In
this paper, we present a probabilistic and decision-theoretic generalisation of
this principle, and we demonstrate how it can be leveraged to build affectively
intelligent artificial agents. The new model, called BayesAct, can maintain
multiple hypotheses about sentiments simultaneously as a probability
distribution, and can make use of an explicit utility function to make
value-directed action choices. This allows the model to generate affectively
intelligent interactions with people by learning about their identity,
predicting their behaviours using the affect control principle, 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 artificially intelligent
systems that interact with humans in two different settings: an exam practice
assistant (tutor) and an assistive device for persons with a cognitive
disability.
