A Hybrid Cognitive Architecture for AGI:  Bridging Symbolic and Subsymbolic AI

Author(s)	Jatin Hans
Country	India
Abstract	This paper presents a novel hybrid cognitive architecture designed to advance Artificial General Intelligence (AGI) by integrating symbolic and subsymbolic AI approaches using graph neural networks (GNN). The architecture comprises several key components: a perception module using neural networks to process raw sensory data, a symbol grounding module to map subsymbolic features to symbolic representations, a symbolic knowledge base for storing facts and rules, a reasoning engine for logical deductions, and a learning module for updating the system based on experience. The GNN serves as an integration layer, connecting symbolic concepts and subsymbolic features to facilitate bidirectional communication, enhancing the system's ability to reason and generalize. This hybrid approach aims to combine the strengths of symbolic AI (logical reasoning) and subsymbolic AI (pattern recognition), offering a promising pathway toward achieving general intelligence, with potential applications in robotics, natural language processing, and visual reasoning.
Keywords	AI, subsymbolic AI, symbolic AI, AGI, GNN, General intelligence
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-17
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.39377
Short DOI	https://doi.org/g882hm

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research