Despite recent progress in artificial intelligence (AI), such as deep neural networks (DNN) and deep reinforcement learning (DRL), critical challenges still exist in incorporating AI into a team with human(s). One of the most important challenges is the need to understand how humans value intermediate decisions (i.e. before they generate a behaviour) through internal models of their confidence, expected reward, risk etc. Critically, such information about human decision-making is not only expressed through overt behaviour, such as speech or action, but more subtlety through physiological changes, small changes in facial expression and posture etc. Socially and emotionally intelligent people are excellent at picking up on this information to infer the current disposition of one another and to guide their decisions and social interactions.
In this project, we propose to develop a physiologically-informed AI platform, utilizing neural and systemic physiological information (e.g. arousal, stress) together with affective cues from facial features to infer latent cognitive and emotional states from humans interacting in a series of social decision-making tasks (e.g. trust game, prisoner’s dilemma etc). Specifically, we will use these latent states to generate rich reinforcement signals to train AI agents (specifically DRL) and allow them to develop a “theory of mind” in order to make predictions about upcoming human behaviour. The ultimate goal of this project is to deliver advancements towards “closing-the-loop”, whereby the AI agent feeds-back its own predictions to the human players in order to optimise behaviour and social interactions.
For more details on essential requirements, funding details and a link to the online application click here. Deadline July 7th, 2019.
