Artificial General Intelligence 9th International Conference, AGI 2016, New York, NY, USA, July 16-19, 2016, Proceedings

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Bibliographic Details
Main Authors Steunebrink, Bas, Wang, Pei, Goertzel, Ben
Format eBook Conference Proceeding
LanguageEnglish
Published Cham Springer Nature 2016
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesLecture Notes in Computer Science
Subjects
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Artificial intelligence-Congresses
Computation by Abstract Devices
Computer Science
Information Systems Applications (incl. Internet)
Pattern Recognition
Special computer methods
Online AccessGet full text
ISBN9783319416496
3319416499
9783319416489
3319416480
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-41649-6

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Table of Contents:
  • 7 Topics for Future Work -- 8 Conclusion -- References -- Imitation Learning as Cause-Effect Reasoning -- 1 Introduction -- 2 Demonstrating Hard-Drive Maintenance -- 3 Imitation Learning with Causal Inference -- 3.1 Learning Skills by Explaining Demonstrations -- 3.2 Imitation and Generalization -- 4 Theoretical and Empirical Results -- 5 Conclusion -- References -- Some Theorems on Incremental Compression -- 1 Introduction -- 2 Preliminaries -- 3 An Example -- 4 Definitions -- 5 Properties of a Single Compression Step -- 6 Orthogonal Feature Bases -- 7 Efficiency of Incremental Compression -- 8 Discussion -- A Proofs -- References -- Rethinking Sigma's Graphical Architecture: An Extension to Neural Networks -- Abstract -- 1 Introduction -- 2 How Did This Come About? -- 3 To What Extent Does It Occur? -- 3.1 Directed Links -- 3.2 Closed-World Semantics -- 3.3 Universal Variables -- 3.4 Filter Nodes -- 3.5 Transform Nodes -- 4 What Are Its Implications (Including to Neural Networks)? -- 5 Conclusion -- Acknowledgments -- References -- Real-Time GA-Based Probabilistic Programming in Application to Robot Control -- Abstract -- 1 Introduction -- 2 Lightweight Implementation of GA-Based Optimization Queries in Probabilistic Programming -- 3 Planning as Probabilistic Programming -- 4 Simultaneous Plan Optimization and Execution -- 5 Conclusion -- Acknowledgements -- References -- About Understanding -- 1 Introduction -- 2 Related Work -- 3 Towards a Theory of Pragmatic Understanding -- 4 Meaning -- 5 A System that Acquires Understanding and Meaning -- 6 Conclusions -- References -- Why Artificial Intelligence Needs a Task Theory -- 1 Introduction -- 2 What We Might Want from a Task Theory -- 3 Requirements for a Task Theory -- 4 What a Task Theory Might Look Like -- 5 Conclusions -- References -- Growing Recursive Self-Improvers -- 1 Introduction
  • 2 Scope and Delineation -- 3 Essential Ingredients of expai -- 4 Recursive Self-Improvement -- 5 Towards a Test Theory -- References -- Different Conceptions of Learning: Function Approximation vs. Self-Organization -- 1 Learning: Different Conceptions -- 2 Learning in NARS -- 3 Comparison and Discussion -- 4 Conclusions -- References -- The Emotional Mechanisms in NARS -- 1 Intelligence and Emotion -- 2 Desirability of Events -- 3 Feelings of the System -- 4 Emotion in Concepts -- 5 Effects of Emotion -- 6 Comparison to Other Approaches -- 7 Comparison to Human Emotions -- 8 Conclusions -- References -- The OpenNARS Implementation of the Non-Axiomatic Reasoning System -- 1 Introduction -- 2 Memory -- 3 Logic Module -- 4 Temporal Inference Control -- 5 Projection and Eternalization -- 6 Anticipation -- 7 Evidence Tracking -- 8 Processing of New and Derived Tasks -- 9 Attentional Control -- 10 Conclusions -- References -- Integrating Symbolic and Sub-symbolic Reasoning -- 1 Introduction -- 2 System Components -- 2.1 Status Signals -- 2.2 Long-Term Memory -- 2.3 Activity -- 2.4 Attention -- 2.5 Working Memory -- 2.6 Decision -- 3 Update Functions -- 3.1 Activity Update -- 3.2 Status Update -- 3.3 Attention Update -- 3.4 WM Update -- 3.5 Decision Update -- 3.6 LTM Update -- 4 Reasoning Mechanisms -- 4.1 Sub-symbolic Reasoning -- 4.2 Symbolic Reasoning -- 5 Prototype Implementation -- 6 Conclusion -- References -- Integrating Axiomatic and Analogical Reasoning -- 1 Introduction -- 2 Mathematical Model -- 2.1 Basic Concepts -- 2.2 Domains -- 2.3 Axiomatic Reasoning -- 2.4 Analogical Reasoning -- 3 System Description -- 4 System Evaluation -- 4.1 Rutherford's Analogy -- 4.2 Natural Language Analogy -- 5 Conclusions -- References -- Embracing Inference as Action: A Step Towards Human-Level Reasoning -- 1 CEC and CECAC -- 1.1 Boxes -- 1.2 Evaluated Codelets
  • 2 Future Work -- References -- Asymptotic Logical Uncertainty and the Benford Test -- 1 Introduction -- 2 Related Work -- 3 The Benford Test -- 4 Irreducible Patterns -- 5 A Learning Algorithm -- 6 Passing the Generalized Benford Test -- 7 Final Remarks -- References -- Towards a Computational Framework for Function-Driven Concept Invention -- 1 Introduction -- 2 Concept Representation -- 3 Computing Blends -- 3.1 Concept Combination -- 3.2 Selecting Concepts -- 3.3 Computational Results -- 4 Related Work -- 5 Conclusions and Future Work -- References -- System Induction Games and Cognitive Modeling as an AGI Methodology -- 1 Introduction -- 2 Related Work -- 3 General Observations on Human SIG-Playing Behavior -- 4 A Model of Early Decision-Making on SIGs -- 4.1 Data -- 4.2 Model -- 4.3 Discussion -- 5 Conclusion -- References -- Integrating Model-Based Prediction and Facial Expressions in the Perception of Emotion -- 1 Introduction -- 2 Method -- 2.1 Expressing Individual Difference in Bayesian Inference -- 2.2 Appraisal Theory and Theory of Mind -- 2.3 Display Rules -- 2.4 Calculation -- 3 Simulation -- 3.1 Context and Model -- 3.2 Display Rules -- 3.3 Experiment -- 4 Simulation Results -- 5 Discussion and Future Work -- References -- A Few Notes on Multiple Theories and Conceptual Jump Size -- 1 Understanding and Learning -- 2 Algorithmic Probability and the Suite of Theories -- 3 Using Bayes' Rule -- 4 Incomputability -- 5 Metamorphoses of a Theory -- 6 Lsearch -- 7 Conceptual Jump Size and Descriptions -- 8 Can the Search Be Practical? -- 9 Agents -- 10 Fun with Unconscious Jumps -- 11 On the Back Porch Just Beyond the Universe -- References -- Generalized Temporal Induction with Temporal Concepts in a Non-axiomatic Reasoning System -- Abstract -- 1 Introduction -- 2 Temporal Concurrency -- 3 Implementation -- 4 Discussion -- 5 Conclusion
  • Intro -- Preface -- Organization -- Contents -- Self-Modification of Policy and Utility Function in Rational Agents -- 1 Introduction -- 2 Preliminaries -- 3 Self Modification Models -- 4 Agents -- 5 Results -- 6 Conclusions -- References -- Avoiding Wireheading with Value Reinforcement Learning -- 1 Introduction -- 2 Setup -- 3 Agent Belief Distributions -- 3.1 Consistency of B and C -- 3.2 Non-Assumptions -- 4 Agent Definitions -- 5 Avoiding Wireheading -- 6 Discussion and Conclusions -- References -- Death and Suicide in Universal Artificial Intelligence -- 1 Introduction -- 2 Preliminaries -- 3 Definitions of Death -- 4 Known Environments: AI -- 5 Unknown Environments: AIXI and AI -- 6 Conclusion -- References -- Ultimate Intelligence Part II: Physical Complexity and Limits of Inductive Inference Systems -- 1 Introduction -- 2 Notation and Background -- 3 Physical Limits to Universal Induction -- 3.1 Logical Depth and Conceptual Jump Size -- 3.2 A Graphical Analysis of Intelligent Computation -- 3.3 Physical Limits, Incremental Learning, and Digital Physics -- References -- Open-Ended Intelligence -- 1 Introduction -- 2 What Is Intelligence? Definition and Critique -- 3 The Theory of Individuation -- 3.1 Assemblages -- 3.2 A New Conceptual Approach to Intelligence -- 4 Intelligence, Cognition, Sense-Making -- 5 A Framework for Open-Ended Intelligence -- 5.1 Structure -- 5.2 The Unfoldment of Individuation -- 5.3 Compatibility, Complexity and OEI -- 5.4 Coordination -- 6 Conclusion -- References -- The AGI Containment Problem -- 1 Introduction -- 2 Motivation -- 2.1 Testing and Experimentation in Safe AGI Development -- 2.2 Emergent Goals of Test AGIs -- 3 Requirements for an AGI Container -- 3.1 Human Factors and Information Hazards -- 4 Defense in Depth -- 5 Light, Medium and Heavy Containment -- 6 Existing Mechanisms
  • 13 Curiosity and Imitation
  • References -- Introspective Agents: Confidence Measures for General Value Functions -- References -- Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Grammar and Production Rules -- 3.2 Probabilities for Production Rules -- 4 Experiments -- 4.1 Samples from Sampled Probabilistic Programs -- 4.2 Learning Sampler Code for Common One-Dimensional Distributions -- 4.3 Evaluating Our Approach Versus Evolutionary Algorithms -- 4.4 Generalising Arbitrary Data Distributions -- 5 Discussion -- References -- How Much Computation and Distributedness is Needed in Sequence Learning Tasks? -- 1 Introduction -- 2 Cellular Automata in Reservoir Computing: ReCA -- 2.1 Encoding Stage -- 2.2 Cellular Automata Reservoir Stage -- 2.3 Read-Out Stage -- 3 Covariance and Stack Representations -- 4 Experiments -- 5 Results and Discussion -- 6 Conclusion -- References -- Analysis of Algorithms and Partial Algorithms -- 1 Introduction: Shortcomings of Traditional Analysis of Algorithms -- 2 Expected-Reward Analysis of Algorithms -- 2.1 Definition -- 2.2 Theory and Practice -- 3 Self-improving AI -- 4 Future Work -- References -- Estimating Cartesian Compression via Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Theoretical Background -- 3.2 Problem Formulation -- 3.3 Numerical Experiment -- 4 Results -- 5 Discussion -- 6 Conclusions -- References -- A Methodology for the Assessment of AI Consciousness -- Abstract -- 1 Methodology -- 2 Instructions -- 3 Ability to Reason and Use Logic -- 4 Situational Awareness -- 5 Natural Language Ability -- 6 Goals, Opinions, and Emotions -- 7 Experiencing Existence -- 8 Growth and Learning -- 9 Self Knowledge -- 10 Self Control -- 11 Knowledge About Humans -- 12 Knowledge About the Current Conversationalist