| Description | 1 online resource |
| Series | Lecture notes in artificial intelligence |
| Lecture notes in computer science ; 12177 | |
| LNCS sublibrary: SL 7, Artificial intelligence | |
| Lecture notes in computer science. Lecture notes in artificial intelligence | |
| Lecture notes in computer science ; | |
| LNCS sublibrary. SL 7, Artificial intelligence | |
| Note | International conference proceedings |
| Includes author index | |
| Contents | Intro — Preface — Organization — Contents — AGI and the Knight-Darwin Law: Why Idealized AGI Reproduction Requires Collaboration — 1 Introduction — 2 The Intuitive Ordinal Notation System — 3 Intuitive Ordinal Intelligence — 4 The Knight-Darwin Law — 4.1 Motivation for Multi-agent Approaches to AGI — 4.2 Motivation for AGI Variety — 4.3 AGI Genetics — 5 Discussion — 5.1 What Does Definition6 Really Have to Do with Intelligence? — 5.2 Can’t an AGI Just Print a Copy of Itself? — 5.3 Prohibitively Expensive Simulation — 6 Conclusion — References — Error-Correction for AI Safety |
| 1 Motivation — 2 Transdisciplinary System Clustering — 3 Type I and Type II AI Safety — 3.1 Type I AI Risks — 3.2 Type II AI Nature and Type II AI Risks — 4 Summary and Outlook — References — Artificial Creativity Augmentation — 1 Deconstructing Anthropic Creativity — 1.1 Creative Outcome in Context — 1.2 Creative Process — 2 Constructing ACA — 2.1 Methods for Anthropic Creativity Augmentation — 2.2 Addressing the Augmentation of Artificial Creativity — 3 Conclusion — References — The Hierarchical Memory Based on Compartmental Spiking Neuron Model — 1 Introduction | |
| 2 The Problem Analysis — 3 The Memory Architecture — 3.1 The Structure — 3.2 The Model Inputs/Outputs — 4 The Experiment — 5 Conclusion — References — The Dynamics of Growing Symbols: A Ludics Approach to Language Design by Autonomous Agents — 1 Introduction — 2 Linking Symbols with Dynamics — 3 Ludics: The Logic of Rules — 3.1 Convergent Perspectives via Orthogonality of Designs — 3.2 Behaviors as Interactive Semantics — 4 Conclusion — References — Approach for Development of Engineering Tools Based on Knowledge Graphs and Context Separation — 1 Introduction | |
| 2 Proposed Approach — 2.1 Context Meaning — 2.2 Class-Object Interchangeability — 3 Examples from Systems Engineering — 3.1 Example of Realization a Structure Application — 3.2 Components Schema App — 4 Discussion — 5 Conclusion — References — Towards Dynamic Process Composition in the DSO Cognitive Architecture — 1 Introduction — 2 Dynamic Process Composition — 3 Anomaly Detection: An Example — 4 Discussion — 5 Conclusion — References — SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners — 1 Introduction — 2 Related Work | |
| 3 SAGE: Overview of Structure and Use — 3.1 Requirements — 3.2 Architecture: Model-View-Controller-Agents (MVC-A) — 4 Proof of Concept — 5 Conclusions and Future Work — References — Post-turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence — 1 Introduction. Turing Methodology for Assessment of Artificial Intelligence (1950-2014) — 2 Methodology for the Critical Analysis of the Turing Test — 3 The Continuum of Turing-Like Tests and Its Limitations — 3.1 From Verbal to Non-verbal — 3.2 From Virtual to Physical — 3.3 Four Areas for AGI Development | |
| Summary | This book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020. The 30 full papers and 8 short papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover topics such as AGI architectures, artificial creativity and AI safety, transfer learning, AI unification and benchmarks for AGI |
| ISBN / ISSN | 9783030521523 |
| 3030521524 | |
| Subject(s) | Artificial intelligence — Congresses |
| Other Author(s) | Goertzel, Ben |
| Panov, Aleksandr I | |
| Potapov, Alexey | |
| Yampolskiy, Roman V., 1979- |
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