Adaptive Neuro-Symbolic Systems for Real Time Ethical Decision-Making in Autonomous Agents

Abstract

With the rapid emergence of autonomous systems, appropriate robust frameworks that could make ethical decisions in real time are needed. The adaptive neuro-symbolic approach to decision making by autonomous agents is thus presented here, integrating the advantages of symbolic ability like conventional AI with the adaptability imparted through neural networks. This proposed system enables symbolic reasoning by the AI along with learning from data, thus ensuring transparency and adaptability in decisions. This system, with deep learning models integrated with symbolic representations, would have an ability to make decisions within complex ethical dilemmas that bring adaptations to dynamic environments with decisions in accordance with ethical principles. Simulations across diverse, real-world scenarios demonstrate the potential of the system in autonomous vehicles, robotics, and other critical decision-making applications. This paper investigates the synergy between symbolic reasoning and neural network learning, aiming to bridge the gap between these paradigms. The hybrid approach combines interpretability and generalization strengths of symbolic AI with the learning strengths of neural networks, thereby overcoming the limitation imposed by the exclusive usage of either approach.