Автор: Pei Wang, Ben Goertzel Название: Theoretical Foundations of Artificial General Intelligence Издательство: Atlantis Press Год: 2016 ISBN: 9789491216619 Серия: Atlantis Thinking Machines (Book 4) Язык: English Формат: pdf Размер: 10,6 mb Страниц: 334
Each chapter focuses on one theoretical problem, proposes a novel solution, and is written in sufficiently non-technical language to be understandable by advanced undergraduates or scientists in allied fields.
This book is the very first collection in the field of Artificial General Intelligence (AGI) focusing on theoretical, conceptual, and philosophical issues in the creation of thinking machines. All the authors are researchers actively developing AGI projects, thus distinguishing the book from much of the theoretical cognitive science and AI literature, which is generally quite divorced from practical AGI system building issues. And the discussions are presented in a way that makes the problems and proposed solutions understandable to a wide readership of non-specialists, providing a distinction from the journal and conference-proceedings literature. The book will benefit AGI researchers and students by giving them a solid orientation in the conceptual foundations of the field (which is not currently available anywhere); and it would benefit researchers in allied fields by giving them a high-level view of the current state of thinking in the AGI field. Furthermore, by addressing key topics in the field in a coherent way, the collection as a whole may play an important role in guiding future research in both theoretical and practical AGI, and in linking AGI research with work in allied disciplines
1. Introduction Pei Wang and Ben Goertzel 1.1 The Matter of Artificial General Intelligence 1.2 The Matter of Theoretical Foundation 1.3 The Matter of Objective 1.4 The Matter of Approach 1.5 Challenges at the Heart of the Matter 1.6 Summary Bibliography
2. Artificial Intelligence and Cognitive Modeling Have the Same Problem Nicholas L Cassirnatis 2.1 The Intelligence Problem 2.2 Existing Methods and Standards are not Sufficient 2.3 Cognitive Modeling: The Model Fit Imperative 2.4 Artificial Intelligence and Cognitive Modeling Can Help Each Other 2.5 Conclusions Bibliography
3. The Piaget-MacGuyver Room Selmer Bringsjord and John Licato 3.1 Introduction 3.2 More on Psychometric AGI 3.3 Descartes’Two Tests 3.4 Piaget’s View of Thinking & The Magnet Test 3.5 The LISA model 3.6 Analogico-Deductive Reasoning in the Magnet Test 3.7 Next Steps Bibliography
4. Beyond the Octopus: From General Intelligence toward a Human-like Mind Sam S. Adams and Steve Burbeck 4.1 Introduction 4.2 Octopus Intelligence 4.3 A “Ladder” of Intelligence 4.4 Linguistic Grounding 4.5 Implications of the Ladder for AGI 4.6 Conclusion Bibliography
5. One Decade of Universal Artificial Intelligence Marcus Hutter 5.1 Introduction 5.2 The AGI Problem 5.3 Universal Artificial Intelligence 5.4 Facets of Intelligence 5.5 Social Questions 5.6 State of the Art 5.7 Discussion Bibliography
6. Deep Reinforcement Learning as Foundation for Artificial General Intelligence Itamar Arel 6.1 Introduction: Decomposing the AGI Problem 6.2 Deep Learning Architectures 6.3 Scaling Decision Making under Uncertainty 6.4 Neuromorphic Devices Scaling AGI 6.5 Conclusions and Outlook Bibliography
7. The LIDA Model as a Foundational Architecture for AGI Usef Faghihi and Stan Franklin 7.1 Introduction 7.2 Why the LIDA Model May Be Suitable for AGI 7.3 LIDA Architecture 7.4 Cognitive Architectures, Features and the LIDA Model 7.5 Discussion, Conclusions Bibliography
8. The Architecture of Human-Like General Intelligence Ben Goertzel, M. Ikle, and J. Wigmore 8.1 Introduction 8.2 Key Ingredients of the Integrative Human-Like Cognitive Architecture 8.3 An Architecture Diagram for Human-Like General Intelligence 8.4 Interpretation and Application of the Integrative Diagram 8.5 Cognitive Synergy 8.6 Why Is It So Hard to Measure Partial Progress Toward Human-Level AGI? 8.7 Conclusion Bibliography
9. A New Constructivist AI Kristinn R. Thorisson 9.1 Introduction 9.2 The Nature of (General) Intelligence 9.3 Constructionist AI: A Critical Look 9.4 The Call for a New Methodology 9.5 Towards a New Constructivist AI 9.6 Conclusions Bibliography
10. Towards an Actual Godel Machine Implementation Bas R. Steunebrink and Jurgen Schmidhuber 10.1 Introduction 10.2 The Godel Machine Concept 10.3 The Theoretical Foundations of Self-Reflective Systems 10.4 Nested Meta-Circular Evaluators 10.5 A Functional Self-Reflective System 10.6 Discussion Appendix: Details of Notation Used Bibliography
11. Artificial General Intelligence Begins with Recognition Tsvi Achler 11.1 Introduction 11.2 Evaluating Flexibility 11.3 Evaluation of Flexibility 11.4 Summary Bibliography
12. Theory Blending as a Framework for Creativity in Systems for General Intelligence Maricarmen Martinez et al. 12.1 Introduction 12.2 Productivity and Cognitive Mechanisms 12.3 Cross-Domain Reasoning 12.4 Basic Foundations of Theory Blending 12.5 The Complex Plane: A Challenging Historical Example 12.6 Outlook for Next Generation General Intelligent Systems 12.7 Conclusions Bibliography
13. Modeling Emotion and Affect Joscha Bach 13.1 Introduction 13.2 Emotion and Affect 13.3 Affective States Emerging from Cognitive Modulation 13.4 Higher-Level Emotions Emerging from Directing Valenced Affects 13.5 Generating Relevance: the Motivational System 13.6 Motive Selection 13.7 Putting it All Together Bibliography
14. AG I and Machine Consciousness Antonio Chella and Riccardo Manzotti 14.1 Introduction 14.2 Consciousness 14.3 Machine Consciousness 14.4 Agent’s Body 14.5 Interactions with the Environment 14.6 Time 14.7 Free Will 14.8 Experience 14.9 Creativity 14.10 Conclusions Bibliography
15. Human and Machine Consciousness as a Boundary Effect in the Concept Analysis Mechanism Richard Loosemore 15.1 Introduction 15.2 The Nature of Explanation 15.3 The Real Meaning of Meaning 15.4 Some Falsifiable Predictions 15.5 Conclusion Bibliography
16. Theories of Artificial Intelligence Pei Wang 16.1 The Problem of AI Theory 16.2 Nature and Content of AI Theories 16.3 Desired Properties of a Theory 16.4 Relations among the Properties 16.5 Issues on the Properties 16.6 Conclusion Bibliography
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