Neuro-Symbolic integration in autonomous robotics: A framework for enhanced decision-making
Year: 2024
Type: article
Abstract: This review paper explores the integration of neuro-symbolic reasoning and deep learning within autonomous robotics, proposing a novel framework to enhance decision-making processes in dynamic environments. The paper begins by examining the challenges AI models face, particularly in context-aware decision-making, and highlights the limitations of existing approaches. It then presents a conceptual model that synergizes the interpretability of symbolic reasoning with the perceptual power of deep learning. This integrated framework is designed to improve real-time contextual understanding, decision-making under uncertainty, and adaptability. The paper also discusses the potential impact of this framework across various industries, such as autonomous vehicles, drones, and healthcare robotics, while outlining future research directions to refine and scale the proposed model. Through this review, the paper aims to contribute to advancing autonomous systems by providing a more robust and interpretable approach to AI-driven decision-making. Keywords: Neuro-Symbolic Integration, Autonomous Robotics, Deep Learning, Symbolic Reasoning, Decision-Making, Symbolic Learning.
Institution: Eastern Michigan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402283461