Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Cognitive architectures and AI thinking models represent foundational frameworks for developing intelligent systems that simulate human-like reasoning, learning, and decision-making. These architectures provide structured methodologies for integrating perception, memory, reasoning, and action into coherent computational systems. This paper provides a comprehensive review of the field, examining classical cognitive architectures such as ACT-R, SOAR, and Sigma, as well asmodern AI thinking models inspired by neural-symbolic systems, hybrid reasoning, and reinforcement learning. Additionally, the paper discusses applications in robotics, natural language understanding, and autonomous decision-making, highlighting the challenges and future directions of cognitive architectures. The synthesis aims to guide researchers and practitioners in designing AI systems with improved adaptability, generalization, and cognitive fidelity.

KEYWORDS: Cognitive architectures, AI thinking models, ACT-R, SOAR, neural-symbolic AI, hybrid reasoning, intelligent systems, human-like cognition