  The presence of artificial agents in human social networks is growing. From
chatbots to robots, human experience in the developed world is moving towards a
socio-technical system in which agents can be technological or biological, with
increasingly blurred distinctions between. Given that emotion is a key element
of human interaction, enabling artificial agents with the ability to reason
about affect is a key stepping stone towards a future in which technological
agents and humans can work together. This paper presents work on building
intelligent computational agents that integrate both emotion and cognition.
These agents are grounded in the well-established social-psychological Bayesian
Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are
motivated in their social interactions by affective alignment: they strive for
their social experiences to be coherent at a deep, emotional level with their
sense of identity and general world views as constructed through culturally
shared symbols. This affective alignment creates cohesive bonds between group
members, and is instrumental for collaborations to solidify as relational group
commitments. BayesAct agents are motivated in their social interactions by a
combination of affective alignment and decision theoretic reasoning, trading
the two off as a function of the uncertainty or unpredictability of the
situation. This paper provides a high-level view of dual process theories and
advances BayesAct as a plausible, computationally tractable model based in
social-psychological theory. We introduce a revised BayesAct model that more
deeply integrates social-psychological theorising, and we demonstrate a
component of the model as being sufficient to account for cognitive biases
about fairness, dissonance and conformity. We show how the model can unify
different exploration strategies in reinforcement learning.
