Robot brains circuits and systems for conscious machines
Presenting practical design guidelines for the creation of non-numeric, autonomous cognitive machines, Robot Brains Circuits and Systems for Conscious Machines examines in detail component parts and realization principles and provides real-world examples for designers, researchers, and advanced stud...
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Chichester
Wiley
2007
J. Wiley John Wiley & Sons, Incorporated Wiley-Interscience Wiley-Blackwell |
| Edition | 1st ed. |
| Subjects | |
| Online Access | Get full text |
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Table of Contents:
- 8 Machine emotions -- 8.1 Introduction -- 8.2 Emotional significance -- 8.3 Pain and pleasure as system reactions -- 8.4 Operation of the emotional soundtrack -- 8.5 Emotional decision making -- 8.6 The system reactions theory of emotions -- 8.6.1 Representational and nonrepresentational modes of operation -- 8.6.2 Emotions as combinations of system reactions -- 8.6.3 The external expressions of emotions -- 8.7 Machine Motivation and willed actions -- 9 Natural language in robot brains -- 9.1 Machine understanding of language -- 9.2 The representation of words -- 9.3 Speech acquisition -- 9.4 The multimodal model of language -- 9.4.1 Overview -- 9.4.2 Vertical grounding of word meaning -- 9.4.3 Horizontal grounding -- syntactic sentence comprehension -- 9.4.4 Combined horizontal and vertical grounding -- 9.4.5 Situation models -- 9.4.6 Pronouns in situation models -- 9.5 Inner speech -- 10 A cognitive architecture for robotbrains -- 10.1 The requirements for cognitive architectures -- 10.2 The Haikonen architecture for robot brains -- 10.3 On hardware requirements -- 11 Machine consciousness -- 11.1 Consciousness in the machine -- 11.1.1 The immateriality of mind -- 11.1.2 The reportability aspect of consciousness -- 11.1.3 Consciousness as internal interaction -- 11.2 Machine perception and qualia -- 11.3 Machine self-consciousness -- 11.3.1 The self as the body -- 11.3.2 The experiencing self -- 11.3.3 Inner speech and consciousness -- 11.3.4 The continuum of the existence of the self -- 11.4 Conscious machines and free will -- 11.5 The ultimate test for machine consciousness -- 11.6 Legal and moral questions -- Epilogue -- The dawn of real machine cognition -- References -- Index
- 4.13 Change direction detection -- 5 Machine perception -- 5.1 General principles -- 5.2 Perception and recognition -- 5.3 Sensors and preprocesses -- 5.4 Perception circuits -- the perception/response feedback loop -- 5.4.1 The perception of a single feature -- 5.4.2 The dynamic behaviour of the perception/response feedback loop -- 5.4.3 Selection of signals -- 5.4.4 Perception/response feedback loops for vectors -- 5.4.5 The perception/response feedback loop as predictor -- 5.5 Kinesthetic perception -- 5.6 Haptic perception -- 5.7 Visual perception -- 5.7.1 Seeing the world out there -- 5.7.2 Visual preprocessing -- 5.7.3 Visual attention and gaze direction -- 5.7.4 Gaze direction and visual memory -- 5.7.5 Object recognition -- 5.7.6 Object size estimation -- 5.7.7 Object distance estimation -- 5.7.8 Visual change detection -- 5.7.9 Motion detection -- 5.8 Auditory perception -- 5.8.1 Perceiving auditory scenes -- 5.8.2 The perception of separate sounds -- 5.8.3 Temporal sound pattern recognition -- 5.8.4 Speech recognition -- 5.8.5 Sound direction perception -- 5.8.6 Sound direction detectors -- 5.8.7 Auditory motion detection -- 5.9 Direction sensing -- 5.10 Creation of mental scenes and maps -- 6 Motor actions for robots -- 6.1 Sensorimotor coordination -- 6.2 Basic motor control -- 6.3 Hierarchical associative control -- 6.4 Gaze direction control -- 6.5 Tracking gaze with a robotic arm -- 6.6 Learning motor action sequences -- 6.7 Delayed learning -- 6.8 Moving towards the gaze direction -- 6.9 Task execution -- 6.10 The quest for cognitive robots -- 7 Machine cognition -- 7.1 Perception, cognition, understanding and models -- 7.2 Attention -- 7.3 Making memories -- 7.3.1 Types of memories -- 7.3.2 Short-term memories -- 7.3.3 Long-term memories -- 7.4 The perception of time -- 7.5 Imagination and planning -- 7.6 Deduction and reasoning
- Intro -- Robot Brains -- Contents -- Preface -- 1 Introduction -- 1.1 General intelligence and conscious machines -- 1.2 How to model cognition? -- 1.3 The approach of this book -- 2 Information, meaning and representation -- 2.1 Meaning and the nonnumeric brain -- 2.2 Representation of information by signal vectors -- 2.2.1 Single signal and distributed signal representations -- 2.2.2 Representation of graded values -- 2.2.3 Representation of significance -- 2.2.4 Continuous versus pulse train signals -- 3 Associative neural networks -- 3.1 Basic circuits -- 3.1.1 The associative function -- 3.1.2 Basic neuron models -- 3.1.3 The Haikonen associative neuron -- 3.1.4 Threshold functions -- 3.1.5 The linear associator -- 3.2 Nonlinear associators -- 3.2.1 The nonlinear associative neuron group -- 3.2.2 Simple binary associator -- 3.2.3 Associator with continuous weight values -- 3.2.4 Bipolar binary associator -- 3.2.5 Hamming distance binary associator -- 3.2.6 Enhanced Hamming distance binary associator -- 3.2.7 Enhanced simple binary associator -- 3.3 Interference in the association of signals and vectors -- 3.4 Recognition and classification by the associative neuron group -- 3.5 Learning -- 3.5.1 Instant Hebbian learning -- 3.5.2 Correlative Hebbian learning -- 3.6 Match, mismatch and novelty -- 3.7 The associative neuron group and noncomputable functions -- 4 Circuit assemblies -- 4.1 The associative neuron group -- 4.2 The inhibit neuron group -- 4.3 Voltage-to-single signal (V/SS) conversion -- 4.4 Single signal-to-voltage (SS/V) conversion -- 4.5 The 'Winner-Takes-All' (WTA) circuit -- 4.6 The 'Accept-and-Hold' (AH) circuit -- 4.7 Synaptic partitioning -- 4.8 Serial-to-parallel transformation -- 4.9 Parallel-to-serial transformation -- 4.10 Associative Predictors and Sequencers -- 4.11 Timing circuits -- 4.12 Timed sequence circuits