Towards personalized human AI interaction - adapting the behavior of AI agents using neural signatures of subjective interest

The use of Artificial Neural Networks (ANNs) towards developing Artificial Intelligence (AI) has undergone a renaissance in the past decade. Out of the many emergent techniques for training ANNs that are collectively referred to as'Deep Learning', Deep Reinforcement Learning (DRL) is proving to be a particularly general and powerful method, with applications ranging from video games [1] to autonomous driving [2]. While most applications of reinforcement learning have traditionally used reinforcement signals derived from performance measures that are explicit to the task - e.g. the score in a game or grammatical errors in a translation, when considering AI systems that are required to have a significant interaction with humans - e.g. the autonomous vehicle - it is critical to consider how the human's preference for objects, events, or actions can be incorporated into the behavioral reinforcement for the AI, particularly in ways that are minimally obtrusive [3], [4]. Such behavioral adaptations occur naturally during social interactions and form the bedrock of social mechanisms that build trust and rapport between strangers [5], [6]. In this paper, we present a novel approach that uses decoded human neurophysiological and ocular time-series data as an implicit reinforcement signal for an AI agent that is driving a virtual automobile.