Foundations of Machine Consciousness in Artificial General Intelligence Systems: Toward Self-Aware and Adaptive Intelligent Machines

Abstract

Machine consciousness is emerging as a critical dimension in the pursuit of Artificial General Intelligence (AGI), aiming to equip machines with self awareness, contextual understanding, and adaptive reasoning capabilities. While modern AI systems excel in perception and data-driven learning, they lack intrinsic awareness and reflective cognition. This paper explores the foundational principles underlying machine consciousness, drawing from interdisciplinary insights in neuroscience, cognitive science, and artificial intelligence. It examines theoretical frameworks such as Global Workspace Theory and Integrated Information Theory, and proposes a hybrid cognitive conscious architecture that integrates perception, memory, attention, and meta cognitive control. The study also highlights computational models, implementation challenges, and potential applications. By establishing a structured foundation, this paper aims to contribute toward the development of self-aware and autonomous AGI systems.

KEYWORDS: Machine Consciousness, Artificial General Intelligence, Cognitive Architecture, Self-Awareness, Meta-Cognition, Intelligent Systems