The computational evolution of cognitive architectures / Iuliia Kotseruba, John K. Tsotsos.

Format:

Book

Author/Creator:

Kotseruba, Iuliia, author.

Tsotsos, John K., author.

Series:

Oxford series on cognitive models and architectures.

Oxford scholarship online.

Oxford series on cognitive models and architectures

Oxford scholarship online

Language:

English

Subjects (All):

Cognition.

Artificial intelligence.

Developmental psychology.

Brain.

Physical Description:

1 online resource (0 pages)

Edition:

1st ed.

Place of Publication:

Oxford : Oxford University Press, [2025]

Summary:

The authors trace the evolution of cognitive architectures, their abilities, and future prospects, from their early logic-based beginnings to their recent melding of classic methodologies with deep learning concepts.

Contents:

Cover

Title Page

Copyright Page

Foreword

Contents

Figures

Tables

What is this book about?

1 What Are Cognitive Architectures?

1.1 Definition and motivation for cognitive architectures

1.1.1 Beyond binary questions

1.1.2 Bridging levels of abstraction

1.1.3 Reverse engineering the mind

1.2 What do cognitive architectures model?

1.2.1 Toward human-level intelligence and beyond

1.2.2 Desiderata

1.3 From theory to software

1.4 Distinguishing frameworks, architectures, and instances

1.5 How many cognitive architectures are there?

1.6 What architectures are covered in this book?

1.7 Summary

2 Cognitive Architectures, AI, and Cognitive Science

2.1 Historical context

2.1.1 Artificial intelligence

2.1.2 Cognitive science

2.2 Roots of cognitive architectures

2.3 Best of both worlds?

2.4 Summary

3 Taxonomies of Cognitive Architectures

3.1 By levels of abstraction and embodiment

3.2 By cognitive conformity

3.3 By representation

3.3.1 Symbolic and subsymbolic

3.3.2 Neurosymbolic integration

3.4 Summary

4 Sensation and Perception

4.1 Sensory modalities

4.1.1 Human senses

4.1.2 Sensory modalities in cognitive architectures

4.2 Environment

4.3 Vision

4.3.1 Sensors

4.3.2 Stages of visual processing

4.3.3 Simplifying visual processing

4.4 Somatosensation, touch, and vestibular sense

4.5 Audition

4.6 Olfaction and gustation

4.7 Other input types

4.8 Multimodal perception

4.9 Perceptual attention

4.9.1 Visual attention

4.9.2 Auditory and other types of attention

4.10 Summary

5 Memory

5.1 Memory types by persistence

5.1.1 Sensory memory

5.1.2 Working memory

5.1.3 Long-term memory

5.2 Memory types by contents

5.2.1 Declarative vs. non-declarative memory

5.2.2 Semantic vs. episodic memory.

5.3 Forgetting

5.4 Summary

6 Learning

6.1 What is learning?

6.1.1 Learning in psychology

6.1.2 Learning in AI

6.2 Types of learning

6.3 Declarative learning

6.3.1 Learning through instruction

6.3.2 Learning by deduction

6.3.3 Learning by induction

6.4 Non-declarative learning

6.4.1 Perceptual learning

6.4.2 Procedural learning

6.4.3 Learning through instruction

6.4.4 Learning by deduction

6.4.5 Learning by induction

6.4.6 Associative and reinforcement learning

6.5 What is needed for learning?

6.6 Non-learning cognitive architectures

6.7 Summary

7 Reasoning and Decision-Making

7.1 What is reasoning?

7.2 Reasoning about beliefs

7.2.1 Monotonic and non-monotonic reasoning

7.2.2 Types of logical inference

7.2.3 Analogical reasoning

7.3 Reasoning about actions

7.3.1 Selecting the best action

7.3.2 Dimensions of difference

7.3.3 Action selection criteria

7.3.4 Behavior modulation

7.4 Reasoning about reasoning

7.4.1 Meta-reasoning abilities

7.4.2 Theory of mind

7.5 Summary

8 Putting It All Together

8.1 Cognitive cycle

8.2 Topology and processing

8.3 Timing

8.3.1 Matching human response time

8.3.2 Matching external events

8.4 Summary

9 Practical Applications of Cognitive Architectures

9.1 Overview of tasks and applications

9.2 Abstract tasks

9.2.1 Perception and attention

9.2.2 Memory

9.2.3 Reasoning

9.2.4 Learning

9.2.5 Multitasking

9.3 Perception and reasoning tasks

9.3.1 Perceptual processing

9.3.2 Playing games and solving puzzles

9.3.3 General problem-solving

9.3.4 Language understanding

9.4 Procedural tasks

9.4.1 Navigation

9.4.2 Reaching and object manipulation

9.4.3 Safety-critical tasks

9.5 Interactive and social tasks

9.5.1 Virtual assistants

9.5.2 Robot assistants.

9.5.3 Social robots

9.6 Real-world and commercial applications

9.7 Summary

10 Evaluating Cognitive Architectures

10.1 Task-based evaluation

10.1.1 Non-comparative evaluation

10.1.2 Comparative evaluation

10.2 Ability-based evaluation

10.2.1 Turing test and beyond

10.2.2 Cognitive and biological plausibility

10.3 Summary

11 Cognitive Architectures in the Deep Learning Era

11.1 What is deep learning?

11.1.1 From connectionism to deep learning

11.1.2 Deep learning and machine learning

11.2 Cognitive science, neuroscience, and deep learning

11.2.1 Cognitive and biological plausibility of deep learning models

11.2.2 Deep neural networks as models of the brain

11.3 Deep learning in cognitive architectures

11.3.1 Modular integration

11.3.2 Representational integration

11.4 Can deep learning result in a cognitive architecture?

11.5 Summary

12 Challenges of the Past and Opportunities Ahead

12.1 Current limitations

12.1.1 Range and realism of cognitive abilities

12.1.2 Evaluation

12.1.3 Reproducibility and replicability

12.1.4 Definitions

12.2 Future directions

12.2.1 General best practices

12.2.2 Open research areas

12.3 Conclusion

References

Index.

Notes:

Includes bibliographical references and index.

Description based on online resource and publisher information; title from PDF title page (viewed on June 13, 2025).

ISBN:

0-19-193337-6

0-19-265927-8

OCLC:

1523262489