Cognitive Architectures for Achieving Artificial General Intelligence: A Unified Framework for Adaptive and Self-Improving Systems

Abstract

Artificial General Intelligence (AGI) represents the next frontier in artificial intelligence, aiming to create systems capable of performing any intellectual task that a human can accomplish. Unlike narrow AI, AGI requires adaptability, reasoning, learning, and contextual understanding across domains. Cognitive architectures serve as the backbone for such systems by providing structured frameworks that simulate human-like cognition. This paper explores various cognitive architectures, their components, and their role in achieving AGI. It proposes a hybrid unified architecture integrating symbolic reasoning, neural learning, and meta-cognitive control. Furthermore, the study analyzes challenges, evaluates architectural models, and presents future research directions for building scalable and safe AGI systems.

KEYWORDS: Artificial General Intelligence, Cognitive Architecture, Meta Learning, Neural-Symbolic Systems, Autonomous Intelligence, Machine Cognition