Hybrid Neuro-Symbolic Cognitive Frameworks For Integrated Intelligence in Artificial General Systems

Abstract

Artificial Intelligence (AI) has evolved significantly from early symbolic reasoning systems to today’s data-driven neural architectures. However, both paradigms possess inherent limitations—symbolic systems struggle with adaptability and scalability, while neural networks lack interpretability and logical reasoning. To bridge this gap, Hybrid Neuro-Symbolic Cognitive Frameworks (HNSCFs) have emerged as a promising direction that unites statistical learning and symbolic logic under a unified cognitive architecture. This paper explores the conceptual foundations, structure, methodologies, and applications of hybrid neuro-symbolic systems, emphasizing their role in achieving human-like cognitive abilities and explainable artificial intelligence (XAI). Furthermore, the study investigates challenges, current research directions, and potential future developments that aim to create more adaptable, interpretable, and generalizable AI systems.

KEYWORDS: Hybrid Neuro-Symbolic Systems, Cognitive Architectures, Artificial General Intelligence (AGI), Explainable AI (XAI), Knowledge Representation, Machine Learning, Symbolic Reasoning, Neural Networks, Cognitive Integration, Deep Learning.