Abstract

We propose a new approach for human-aware Artificial Intelligence (AI) systems and human augmentation based on the Johari Window theory. For effective and safe interaction, humans and AI systems, such as mobile robots, must share common goals, have a mutual understanding of each other, and know relevant aspects of each other's current states. The interaction between the human and the robot is also the mechanism for moving information between the Johari window panes associated with the human or robot. According to Johari Window Theory the goal of good communication is to expand the “Open area” square both horizontally and vertically. Vertical expansion occurs when we include information based on personalization, and horizontal expansion occurs via a structured trial-and-error process called reinforcement learning. For personalization, we propose to detect and recognize the humans’ emotions and gestures, so the robot can respond accordingly. For reinforcement learning, we propose to develop a new model we call, multi-focus attention deep reinforcement learning, which is based on a control mechanism presented by Kahneman’s Theory of two systems, or “Thinking Fast and Slow“. If robots can “read” gestures of a human, this will determine the success of a task, and learning will occur through trial-and-error or reinforcement learning. For example, when the human becomes more aware of whether his/her facial expressions and gestures match the intent; this expands information vertically into the Johari window pane for the human. When the human’s gestures and facial expressions become consistent with intent, the information will also appear in the open Johari window pane for the robot. In this way, trusted communication between humans and robots maximizes performance and safety.

Keywords

human -robot interaction, johari window model, robots, communication, behaviors, success, human, information, vertically, expressions, facial, gestures, appear, performance, safety