Human-Centered AI using Ethical Causality and Learning Representation for Multi-Agent Deep Reinforcement Learning

Abstract

Human-Centered Computing and AI are two fields devoted to several cross-intersecting interests in the modern AI design. They consider human factors and the machine learning algorithms to enhance compatibility and reliability for human-robot interaction and cooperation. In this work, we propose a novel design concept for the challenging issues that have raised ethical dilemmas; an augmented ethical causality with successor representation for policy gradient models Human-Centered AI with environments. The proposed system leverages Human-Centered AI for using explainable knowledge to construct the ethical causality, and shows it significantly outperformed the statistical approach and baselines alone by further considering meta parametric Human-Centered ethical priorities, when compared to other approaches in the simulated game theory Deep Reinforcement Learning environments. The experimental results aim to efficiently and effectively access the cause, effect and impact of causal inference and multi-agent heterogeneity in the DRL environments for natural, general and significant causal learning representations.