Neuro-Symbolic AI Models for Complex Decision Making in Autonomous Environments
Authors:-Surya.S
Abstract-The advent of Neuro-Symbolic AI models has revolutionized the approach to complex decision-making in autonomous environments. These models combine the strengths of neural networks and symbolic reasoning to tackle problems that require both data-driven learning and human-like reasoning. In this paper, we explore the integration of these two paradigms and their potential for improving decision-making in dynamic, real-world autonomous systems. We begin by outlining the fundamental principles of Neuro-Symbolic AI, discussing how it bridges the gap between purely data-driven deep learning models and rule-based symbolic systems. We highlight key challenges in autonomous decision-making, such as uncertainty, partial observability, and the need for interpretability. The paper then presents a framework for applying Neuro-Symbolic models to decision-making tasks, illustrating their capabilities in handling complex environments such as robotics, self-driving cars, and smart grids. Furthermore, we examine case studies that demonstrate the practical applications of these models in various autonomous systems, showcasing their potential to outperform traditional AI approaches. The paper concludes by discussing the future prospects of Neuro-Symbolic AI, including the challenges that need to be addressed, such as scalability, learning efficiency, and integration with existing autonomous systems. Ultimately, the paper aims to provide a comprehensive understanding of how Neuro-Symbolic AI models can significantly enhance decision-making processes in autonomous environments.
