Foundations of Cognitive Architectures: Interdisciplinary Models of Cognition for Artificial Intelligence

This book is a comprehensive guide to designing such architectures from the ground up, combining insights from artificial intelligence, neuroscience, psychology, and systems engineering.

With 25 chapters covering everything from symbolic and subsymbolic models to robotics, language, ethics, and multi-agent systems, it equips you with the theory, structure, and practical tools needed to build cognitive systems that scale, adapt, and evolve.

💡 What You’ll Learn

The evolution of cognitive modeling—from symbolic AI to hybrid and biologically inspired systems

Core components of cognition: memory, learning, attention, planning, and reasoning

Symbolic, subsymbolic, and hybrid frameworks—and how to integrate them

Temporal cognition, sequence learning, and agent-level decision-making

Real-time robotics, multi-agent systems, human-computer interaction, and language

Security, ethics, cultural adaptation, and context-aware cognition

Comparative studies of major architectures: ACT-R, SOAR, CLARION, and beyond

Tools, simulators, and best practices for scalable, testable development

🎯 Who This Book Is For

AI Researchers & AGI Theorists seeking systems-level integration

Engineers & Developers building autonomous agents, HCI, or robotics

Graduate Students in AI, neuroscience, cognitive science, and computational psychology

Cognitive Modelers exploring the link between architecture and human intelligence

Whether you're building adaptive robotics, exploring AGI, or designing explainable systems, this is the foundation—built for those who engineer intelligence, not just simulate it.