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Artificial General Intelligence

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Cover of 'Artificial General Intelligence'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Self-Modification of Policy and Utility Function in Rational Agents
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    Chapter 2 Avoiding Wireheading with Value Reinforcement Learning
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    Chapter 3 Death and Suicide in Universal Artificial Intelligence
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    Chapter 4 Ultimate Intelligence Part II: Physical Complexity and Limits of Inductive Inference Systems
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    Chapter 5 Open-Ended Intelligence
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    Chapter 6 The AGI Containment Problem
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    Chapter 7 Imitation Learning as Cause-Effect Reasoning
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    Chapter 8 Some Theorems on Incremental Compression
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    Chapter 9 Rethinking Sigma’s Graphical Architecture: An Extension to Neural Networks
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    Chapter 10 Real-Time GA-Based Probabilistic Programming in Application to Robot Control
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    Chapter 11 About Understanding
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    Chapter 12 Why Artificial Intelligence Needs a Task Theory
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    Chapter 13 Growing Recursive Self-Improvers
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    Chapter 14 Different Conceptions of Learning: Function Approximation vs. Self-Organization
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    Chapter 15 The Emotional Mechanisms in NARS
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    Chapter 16 The OpenNARS Implementation of the Non-Axiomatic Reasoning System
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    Chapter 17 Integrating Symbolic and Sub-symbolic Reasoning
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    Chapter 18 Integrating Axiomatic and Analogical Reasoning
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    Chapter 19 Embracing Inference as Action: A Step Towards Human-Level Reasoning
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    Chapter 20 Asymptotic Logical Uncertainty and the Benford Test
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    Chapter 21 Towards a Computational Framework for Function-Driven Concept Invention
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    Chapter 22 System Induction Games and Cognitive Modeling as an AGI Methodology
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    Chapter 23 Integrating Model-Based Prediction and Facial Expressions in the Perception of Emotion
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    Chapter 24 A Few Notes on Multiple Theories and Conceptual Jump Size
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    Chapter 25 Generalized Temporal Induction with Temporal Concepts in a Non-axiomatic Reasoning System
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    Chapter 26 Introspective Agents: Confidence Measures for General Value Functions
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    Chapter 27 Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation
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    Chapter 28 How Much Computation and Distributedness is Needed in Sequence Learning Tasks?
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    Chapter 29 Analysis of Algorithms and Partial Algorithms
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    Chapter 30 Estimating Cartesian Compression via Deep Learning
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    Chapter 31 A Methodology for the Assessment of AI Consciousness
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    Chapter 32 Toward Human-Level Massively-Parallel Neural Networks with Hodgkin-Huxley Neurons
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    Chapter 33 Modeling Neuromodulation as a Framework to Integrate Uncertainty in General Cognitive Architectures
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    Chapter 34 Controlling Combinatorial Explosion in Inference via Synergy with Nonlinear-Dynamical Attention Allocation
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    Chapter 35 Probabilistic Growth and Mining of Combinations: A Unifying Meta-Algorithm for Practical General Intelligence
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    Chapter 36 Ideas for a Reinforcement Learning Algorithm that Learns Programs
Attention for Chapter 16: The OpenNARS Implementation of the Non-Axiomatic Reasoning System
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Chapter title
The OpenNARS Implementation of the Non-Axiomatic Reasoning System
Chapter number 16
Book title
Artificial General Intelligence
Published in
Lecture notes in computer science, June 2016
DOI 10.1007/978-3-319-41649-6_16
Book ISBNs
978-3-31-941648-9, 978-3-31-941649-6
Authors

Patrick Hammer, Tony Lofthouse, Pei Wang

Editors

Bas Steunebrink, Pei Wang, Ben Goertzel

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 27%
Student > Master 3 20%
Student > Postgraduate 2 13%
Researcher 2 13%
Professor 1 7%
Other 1 7%
Unknown 2 13%
Readers by discipline Count As %
Computer Science 6 40%
Engineering 2 13%
Unspecified 1 7%
Philosophy 1 7%
Physics and Astronomy 1 7%
Other 1 7%
Unknown 3 20%