Artificial general intelligence : 13th International Conference, AGI 2020, St. Petersburg, Russia, September 16-19, 2020, Proceedings

Artificial general intelligence : 13th International Conference, AGI 2020, St. Petersburg, Russia, September 16-19, 2020, Proceedings /

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Bibliographic Details
Corporate Authors: AGI (Conference) Saint Petersburg, Russia), ProQuest (Firm)
Other Authors: Goertzel, Ben, Panov, Aleksandr I., Potapov, Alexey, Yampolskiy, Roman V., 1979-
Format: Electronic Conference Proceeding eBook
Language:English
Published: Cham : Springer, 2020.
Series:Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12177.
LNCS sublibrary. Artificial intelligence.
Subjects:
Artificial intelligence > Congresses.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of 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

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