A Biologically-Inspired Neural Implementation of Affect Control Theory
Social interactions are a part of day-to-day life of most human beings. Affect, decision-making and behavior are central to it. With increase in adaptation of technology in our society, interaction between humans and artificially intelligent agents is also increasing. Large-scale brain-inspired neural models have been equipped with capabilities to fulfil a variety of tasks, but there has been relatively limited focus on making them capable of handling social interaction. In this paper, NeuroACT, a neural computational model and implementation of a socio-psychological theory called Affect Control Theory (ACT) is presented. This is towards building an emotionally intelligent AI agent, that can handle interactions. It takes as input a continuous affective interpretation of a perceived event, consisting of an actor, behavior and an object and generates post-event predictions of the next optimal behavior to minimize deflection. The aim is to model the role of affect guiding decision-making in AI agents, resulting in interactions that are similar to human interactions, while inhibiting some behaviors based on the social context.