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Oxford – The Computational Evolution of Cognitive Architectures 2025

Updated August 10, 2026 7.3 MB
Oxford – The Computational Evolution of Cognitive Architectures 2025

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The Computational Evolution of Cognitive Architectures examines the history and evolution of cognitive architectures from early logic-based models to modern systems integrated with deep learning. By analyzing more than 3,000 scientific papers, the authors have attempted to map out a roadmap for how computers can simulate the human mind and discuss the challenges facing artificial general intelligence (AGI).

This book weaves together philosophical, neuroscientific, and computer science concepts to provide a deep understanding of the structures of memory, decision-making, and learning in machines. This is a valuable and inspiring resource for researchers seeking to understand the origins of artificial intelligence and how mental models evolve in digital systems.

Book Features

  • Comprehensive analysis of over 80 different cognitive architectures over the past decades.
  • Exploring the interaction between classical (symbolic) artificial intelligence and modern deep learning.
  • Focusing on common components of the human mind and computational models such as memory and attention.
  • Investigating the application of cognitive architectures in robotics and intelligent decision-making systems.
  • Discussion on the future of artificial intelligence and the move towards models similar to human behavior.
  • Rich scholarly reference including extensive citations and references to cognitive science literature.

Book Specifications The Computational Evolution of Cognitive Architectures

  • Publisher: Oxford
  • Instructor/Author: IULIIA KOTSERUBA
  • Number of pages: 305
  • Number of chapters: 12
  • Format: PDF

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The Computational Evolution of Cognitive Architectures

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The Computational Evolution of Cognitive Architectures

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What is included

  • Comprehensive analysis of over 80 different cognitive architectures over the past decades.
  • Exploring the interaction between classical (symbolic) artificial intelligence and modern deep learning.
  • Focusing on common components of the human mind and computational models such as memory and attention.
  • Investigating the application of cognitive architectures in robotics and intelligent decision-making systems.
  • Discussion on the future of artificial intelligence and the move towards models similar to human behavior.
  • Rich scholarly reference including extensive citations and references to cognitive science literature.