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Robot Brains: Circuits and Systems for Conscious Machines

Book Description

Haikonen envisions autonomous robots that perceive and understand the world directly, acting in it in a natural human-like way without the need of programs and numerical representation of information. By developing higher-level cognitive functions through the power of artificial associative neuron architectures, the author approaches the issues of machine consciousness.

Robot Brains expertly outlines a complete system approach to cognitive machines, offering practical design guidelines for the creation of non-numeric autonomous creative machines. It details topics such as component parts and realization principles, so that different pieces may be implemented in hardware or software. Real-world examples for designers and researchers are provided, including circuit and systems examples that few books on this topic give.

In novel technical and practical detail, this book also considers:

  • the limitations and remedies of traditional neural associators in creating true machine cognition;

  • basic circuit assemblies cognitive neural architectures;

  • how motors can be interfaced with the associative neural system in order for fluent motion to be achieved without numeric computations;

  • memorization, imagination, planning and reasoning in the machine;

  • the concept of machine emotions for motivation and value systems;

  • an approach towards the use and understanding of natural language in robots.

The methods presented in this book have important implications for computer vision, signal processing, speech recognition and other information technology fields. Systematic and thoroughly logical, it will appeal to practising engineers involved in the development and design of robots and cognitive machines, also researchers in Artificial Intelligence. Postgraduate students in computational neuroscience and robotics, and neuromorphic engineers will find it an exciting source of information.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. 1: Introduction
    1. 1.1 GENERAL INTELLIGENCE AND CONSCIOUS MACHINES
    2. 1.2 HOW TO MODEL COGNITION?
    3. 1.3 THE APPROACH OF THIS BOOK
  7. 2: Information, meaning and representation
    1. 2.1 MEANING AND THE NONNUMERIC BRAIN
    2. 2.2 REPRESENTATION OF INFORMATION BY SIGNAL VECTORS
  8. 3: Associative neural networks
    1. 3.1 BASIC CIRCUITS
    2. 3.2 NONLINEAR ASSOCIATORS
    3. 3.3 INTERFERENCE IN THE ASSOCIATION OF SIGNALS AND VECTORS
    4. 3.4 RECOGNITION AND CLASSIFICATION BY THE ASSOCIATIVE NEURON GROUP
    5. 3.5 LEARNING
    6. 3.6 MATCH, MISMATCH AND NOVELTY
    7. 3.7 THE ASSOCIATIVE NEURON GROUP AND NONCOMPUTABLE FUNCTIONS
  9. 4: Circuit assemblies
    1. 4.1 THE ASSOCIATIVE NEURON GROUP
    2. 4.2 THE INHIBIT NEURON GROUP
    3. 4.3 VOLTAGE-TO-SINGLE SIGNAL (V/SS) CONVERSION
    4. 4.4 SINGLE SIGNAL-TO-VOLTAGE (SS/V) CONVERSION
    5. 4.5 THE ‘WINNER-TAKES-ALL’ (WTA) CIRCUIT
    6. 4.6 THE ‘ACCEPT-AND-HOLD’ (AH) CIRCUIT
    7. 4.7 SYNAPTIC PARTITIONING
    8. 4.8 SERIAL-TO-PARALLEL TRANSFORMATION
    9. 4.9 PARALLEL-TO-SERIAL TRANSFORMATION
    10. 4.10 ASSOCIATIVE PREDICTORS AND SEQUENCERS
    11. 4.11 TIMING CIRCUITS
    12. 4.12 TIMED SEQUENCE CIRCUITS
    13. 4.13 CHANGE DIRECTION DETECTION
  10. 5: Machine perception
    1. 5.1 GENERAL PRINCIPLES
    2. 5.2 PERCEPTION AND RECOGNITION
    3. 5.3 SENSORS AND PREPROCESSES
    4. 5.4 PERCEPTION CIRCUITS; THE PERCEPTION/RESPONSE FEEDBACK LOOP
    5. 5.5 KINESTHETIC PERCEPTION
    6. 5.6 HAPTIC PERCEPTION
    7. 5.7 VISUAL PERCEPTION
    8. 5.8 AUDITORY PERCEPTION
    9. 5.9 DIRECTION SENSING
    10. 5.10 CREATION OF MENTAL SCENES AND MAPS
  11. 6: Motor actions for robots
    1. 6.1 SENSORIMOTOR COORDINATION
    2. 6.2 BASIC MOTOR CONTROL
    3. 6.3 HIERARCHICAL ASSOCIATIVE CONTROL
    4. 6.4 GAZE DIRECTION CONTROL
    5. 6.5 TRACKING GAZE WITH A ROBOTIC ARM
    6. 6.6 LEARNING MOTOR ACTION SEQUENCES
    7. 6.7 DELAYED LEARNING
    8. 6.8 MOVING TOWARDS THE GAZE DIRECTION
    9. 6.9 TASK EXECUTION
    10. 6.10 THE QUEST FOR COGNITIVE ROBOTS
  12. 7: Machine cognition
    1. 7.1 PERCEPTION, COGNITION, UNDERSTANDING AND MODELS
    2. 7.2 ATTENTION
    3. 7.3 MAKING MEMORIES
    4. 7.4 THE PERCEPTION OF TIME
    5. 7.5 IMAGINATION AND PLANNING
    6. 7.6 DEDUCTION AND REASONING
  13. 8: Machine emotions
    1. 8.1 INTRODUCTION
    2. 8.2 EMOTIONAL SIGNIFICANCE
    3. 8.3 PAIN AND PLEASURE AS SYSTEM REACTIONS
    4. 8.4 OPERATION OF THE EMOTIONAL SOUNDTRACK
    5. 8.5 EMOTIONAL DECISION MAKING
    6. 8.6 THE SYSTEM REACTIONS THEORY OF EMOTIONS
    7. 8.7 MACHINE MOTIVATION AND WILLED ACTIONS
  14. 9: Natural language in robot brains
    1. 9.1 MACHINE UNDERSTANDING OF LANGUAGE
    2. 9.2 THE REPRESENTATION OF WORDS
    3. 9.3 SPEECH ACQUISITION
    4. 9.4 THE MULTIMODAL MODEL OF LANGUAGE
    5. 9.5 INNER SPEECH
  15. 10: A cognitive architecture for robot brains
    1. 10.1 THE REQUIREMENTS FOR COGNITIVE ARCHITECTURES
    2. 10.2 THE HAIKONEN ARCHITECTURE FOR ROBOT BRAINS
    3. 10.3 ON HARDWARE REQUIREMENTS
  16. 11: Machine consciousness
    1. 11.1 CONSCIOUSNESS IN THE MACHINE
    2. 11.2 MACHINE PERCEPTION AND QUALIA
    3. 11.3 MACHINE SELF-CONSCIOUSNESS
    4. 11.4 CONSCIOUS MACHINES AND FREE WILL
    5. 11.5 THE ULTIMATE TEST FOR MACHINE CONSCIOUSNESS
    6. 11.6 LEGAL AND MORAL QUESTIONS
  17. Epilogue
    1. THE DAWN OF REAL MACHINE COGNITION
  18. References
  19. Index