Note: Intro -- Preface -- Organization -- Table of Contents -- Full Papers -- Utilizing Cognitive Mechanisms in the Analysis of Counterfactual Conditionals by AGI Systems -- 1 Counterfactual Conditionals (CFC) -- 2 Cognitive Mechanisms and Counterfactual Conditionals -- 3 A Blending-Based Formal Treatment -- 4 The Caesar-Korean Blends: A CFC Example -- 5 Concluding Remarks -- References -- When Almost Is Not Even Close: Remarks on the Approximability of HDTP -- References -- Lojban++: An Interlingua for Communication between Humans and AGIs -- 1 Introduction -- 2 Lojban versus Lojban++ -- 3 Some Simple Examples -- 4 The Need for Lojban Software -- 5 Lojban and Inference -- 6 Discussion -- References -- Integrating Feature Selectionin to Program Learning -- 1 Introduction -- 2 Data- and Feature- focusable Learning Problems -- 3 Integrating Feature Selection into Learning -- 4 Integrating Feature Selection into MOSES Learning -- 5 Application to Genomic Data Classification -- 6 Conclusion -- References -- Integrating Deep Learning Based Perception with Probabilistic Logic via Frequent Pattern Mining -- 1 Introduction -- 2 Using Subtree Mining to Bridge the Gap between DeSTIN and PLN -- 3 Some Simple Experiments with Letters -- References -- Predictive Heuristics for Decision-Making in Real-World Environments -- 1 Introduction -- 2 Traditional Heuristic Search -- 3 Challenges of Real-World Environments -- 4 Adapting Search to Real-World Environments -- 5 Predictive Heuristics -- 6 Discussion -- References -- Knowledge Representation, Learning, and Problem Solving for General Intelligence -- 1 Introduction -- 2 From Visual Learning to Action Plan Generation -- 3 Generalizations of the Representational Scheme to Motivational Problem Solving -- 4 Discussion and Conclusions -- References -- Metacomputations and Program-Based Knowledge Representation. , 1 Introduction -- 2 Specialization and Futamura Projections -- 3 Another Application of Specializer -- 4 Supercompilation and Inversion -- 5 Conclusion -- References -- Towards a Programming Paradigm for Control Systems with High Levels of Existential Autonomy -- 1 Introduction -- 2 Key Requirements for Existential Autonomy -- 3 Overview of Replicode -- 4 Model Execution and Memory Management -- 5 Learning -- 6 Control Mechanisms -- 7 Conclusion and Future Work -- References -- Universal Induction with Varying Sets of Combinators -- 1 Introduction -- 2 Combinatory Logic as the Family of Reference Machines -- 3 Genetic Programming with CL -- 4 Experiments -- 5 Conclusion -- References -- Modeling Two-Player Games in the Sigma Graphical Cognitive Architecture -- 1 Introduction -- 2 Sigma -- 3 Single-Stage Simultaneous-Move Games -- 4 Sequential Games: The Ultimatum Game -- 5 Conclusion -- References -- Model Construction in General Intelligence -- 1 Theoretical Considerations -- 2 Two Examples of Constructing and Thinking with Models -- 3 Discussion: Aspects and Assets of Model Construction -- 4 Conclusions -- References -- Resource-Bounded Machines are Motivated to be Effective, Efficient, and Curious -- 1 Introduction -- 2 Fundamental Resources -- 3 Resource Compression: What and Why -- 4 Driven by Resource Compression Progress -- 5 An Operational Framework for Resource-Bounded Machines -- 6 AERA: An Explicitly Resource-Bounded Architecture -- 7 Discussion and Conclusion -- References -- Bounded Kolmogorov Complexity Based on Cognitive Models -- 1 PatternDiscovery -- 1.1 Number Sequence Problems -- 1.2 Kolmogorov Complexity -- 1.3 Structure of the Paper -- 2 Bounded Kolmogorov Complexity -- 3 Computational Model -- 3.1 Terms -- 3.2 Bounded Computations -- 3.3 Subsequences -- 3.4 Index Shifts -- 3.5 Term Preference -- 3.6 Implementation -- 4 Results. , 5 Discussion -- 6 Conclusion -- References -- A Cognitive Architecture Based on Dual Process Theory -- 1 Introduction -- 2 Cognitive Modeling -- 3 Cognitive Architecture -- 4 Computations -- 5 Conclusion -- References -- Learning Agents with Evolving Hypothesis Classes -- 1 Introduction -- 2 Overview of the Framework -- 3 Optimistic Agents -- 4 Suggesting Laws -- 5 Human Cognitive Biases and Practical Planning -- 6 Philosophical Issues -- 7 Conclusions -- References -- Natural Language Processing by Reasoning and Learning -- 1 Introduction -- 2 Knowledge Representation -- 3 Inference Rules -- 4 MemoryandControl -- 5 NLP as Reasoning -- 6 Comparisons -- References -- Technical Communications -- A Note on Tractability and Artificial Intelligence -- References -- Human-Level Artificial Intelligence Must Be a Science -- References -- The Role of Specialized Intelligent Body-System Networks in Guiding General-Purpose Cognition -- 1 Introduction -- 2 Some of the Human Body's Specialized Intelligent Subsystems -- 3 Implications for AGI -- References -- Special Session on Cognitive Robotics and AGI -- Knowledgeable Talking Robots -- 1 Introduction -- 2 Related Work -- 3 System Overview -- 4 Knowledge Acquisition and Task Execution -- 5 Discussion -- References -- Cognitivist and Emergent Cognition - An Alternative Perspective -- 1 The Embodiment Divide -- 2 Symbolic Emergent Systems -- References -- Skill Learning and Inference Framework -- 1 Introduction -- 2 Skill Learning and Inference Framework -- 3 Experimental Results -- 4 Conclusion -- References -- Author Index.

Note: Intro -- Preface -- Acknowledgments -- Contents -- Acronyms -- 1 Introduction and Preliminaries -- 1.1 Systems Biology -- 1.1.1 Overviews -- 1.1.2 Developments -- 1.1.3 Implications and Applications -- 1.2 Complex Networks -- 1.2.1 Overviews -- 1.2.2 Mathematical Description -- 1.2.3 Four Types of Networks -- 1.2.3.1 Regular Networks -- 1.2.3.2 Erdös-Rényi (ER) Random Networks -- 1.2.3.3 Scale-Free Networks -- 1.2.3.4 Small-World Networks -- 1.2.4 Statistical Metrics of Networks -- 1.2.4.1 Average Degree and Degree Distribution -- 1.2.4.2 Average Path Length -- 1.2.4.3 Diameter -- 1.2.4.4 Assortativity and Disassortativity -- 1.2.4.5 Small Worldness -- 1.2.4.6 Hierarchical Modularity -- 1.2.4.7 Modularity -- 1.2.4.8 Network Structure Entropy -- 1.2.5 Datasets for Real-World Complex Networks -- 1.3 Central Dogma of Molecular Biology -- 1.4 Bio-Molecular Networks -- 1.5 Several Statistical Methods -- 1.5.1 Descriptive Statistics -- 1.5.2 Cluster Analysis -- 1.5.2.1 Hierarchical Clustering -- 1.5.2.2 k-Means Clustering -- 1.5.3 Principal Component Analysis -- 1.6 Software for Network Visualization and Analysis -- 1.6.1 Pajek -- 1.6.2 Gephi -- 1.6.3 Cytoscape -- 1.6.4 MATLAB Packages and Others -- 1.7 Software for Statistical and Dynamical Analysis -- 1.7.1 SAS -- 1.7.2 SPSS -- 1.7.3 MATLAB -- 1.7.4 R -- 1.7.5 Some Other Software -- 1.7.5.1 Small Software for Clustering Analysis -- 1.7.5.2 Venn Diagrams -- 1.7.5.3 Software for Bifurcation and Dynamical Analysis -- 1.8 Organization of the Book -- References -- Part I Modeling and Dynamical Analysis of Bio-molecular Networks -- 2 Reconstruction of Bio-molecular Networks -- 2.1 Backgrounds -- 2.2 Reconstruction of Bio-molecular Networks Based on Online Databases -- 2.2.1 Regulatory Networks -- 2.2.2 Protein-Protein Interaction Networks -- 2.2.3 Signal Transduction Networks -- 2.2.4 Metabolic Networks. , 2.3 Artificial Algorithms for Generating Bio-molecular Networks -- 2.3.1 Algorithms for Artificial Regulatory Networks -- 2.3.2 Algorithms for Artificial PPI Networks -- 2.4 Statistical Reconstruction of Bio-molecular Networks -- 2.4.1 Association Methods -- 2.4.1.1 Various Similarity Measures -- 2.4.1.2 The Mean Variance Method -- 2.4.2 Information Theoretic Approaches -- 2.4.3 Partial Correlation/Gaussian Graphical Models -- 2.4.4 Granger Causality Methods -- 2.4.4.1 Granger Causality -- 2.4.4.2 Partial Granger Causality -- 2.4.4.3 Windowed Granger Causality -- 2.4.5 Statistical Regression Methods -- 2.4.6 Bayesian Methods -- 2.4.7 Variational Bayesian Methods -- 2.5 Topological Identification via Dynamical Networks -- 2.6 Discussions and Conclusions -- References -- 3 Modeling and Analysis of Simple Genetic Circuits -- 3.1 Backgrounds -- 3.2 Mathematical Modeling Techniques of Biological Networks -- 3.2.1 The Chemical Master Equation -- 3.2.2 Stochastic Simulation Algorithms -- 3.2.3 The Chemical Langevin Equation -- 3.2.4 Numerical Regimes for Stochastic Differential Equations -- 3.2.5 The Reaction Rate Equation -- 3.2.6 Numerical Regimes for Ordinary Differential Equations -- 3.3 Network Motifs and Motif Detection -- 3.4 The Feed-Forward Genetic Circuits -- 3.4.1 Related Works and Motivations -- 3.4.2 Methods for Parameter Sensitivities Analysis -- 3.4.2.1 Local Relative Parameter Sensitivities -- 3.4.2.2 A Traditional GPS Method: RS-HDMR -- 3.4.2.3 The New Global Relative Parameter Sensitivities Approach -- 3.4.3 Global Relative Parameter Sensitivities of the FFLs -- 3.4.3.1 Mathematical Models for the FFLs in GRNs -- 3.4.3.2 The GRPS of the FFLs -- 3.4.3.3 The Global Relative Parameter Sensitivities of CFFLs -- 3.4.3.4 The Global Relative Parameter Sensitivities of ICFFLs -- 3.4.3.5 The Effect of Input x on GRPS. , 3.4.3.6 The Effect of the Hill Coefficient n on the GRPS -- 3.4.3.7 RS-HDMR Versus GRPS on FFLs -- 3.4.4 GRPS and Biological Functions of the FFLs -- 3.4.4.1 GRPS and Biological Abundance of FFLs -- 3.4.4.2 Relations Between GRPS and Noise Characteristics -- 3.4.4.3 GRPS and Fold-Change Detection -- 3.4.5 Global Relative Input-Output Analysis of the FFLs -- 3.4.5.1 A GRIOS Index -- 3.4.5.2 GRIOS of the FFLs -- 3.4.5.3 GRIOS of the FFLs Versus Its Structural and Functional Characteristics -- 3.4.6 Summary -- 3.5 The Coupled Positive and Negative Feedback Genetic Circuits -- 3.5.1 Related Works and Motivations -- 3.5.2 Mathematical Models -- 3.5.2.1 Deterministic Models: Without Time Delay -- 3.5.2.2 Deterministic Models with Time Delays -- 3.5.2.3 Stochastic Model Directly from the Deterministic ODE: The Undeveloped Case -- 3.5.2.4 Stochastic Model from Table 3.9: The Developed Case -- 3.5.2.5 Stochastic Simulations -- 3.5.3 Dynamical Analysis and Functions -- 3.5.3.1 Bifurcation Analysis -- 3.5.3.2 Molecular Noise -- 3.5.3.3 Deterministic Versus Stochastic Dynamics for Parameters Near the Deterministic Bifurcation Points -- 3.5.3.4 Deterministic Versus Stochastic Dynamics for Parameters Locating in the Deterministic Excitable Region -- 3.5.3.5 Deterministic Versus Stochastic Dynamics for Parameters Locating in the Deterministic Bistable Region -- 3.5.3.6 Deterministic Versus Stochastic Dynamics for Parameters Locating in the Deterministic Oscillation Region -- 3.5.4 Summary -- 3.6 The Multi-Positive Feedback Circuits -- 3.6.1 Related Works and Motivations -- 3.6.2 Mathematical Models -- 3.6.3 Dynamical Analysis and Functions -- 3.6.3.1 The APFL Strength Can Tune the Size of the Bistable Region -- 3.6.3.2 The APFL Can Tune the Attractiveness of the Stable Steady States -- 3.6.3.3 The APFL Can Change the Global Relative I/O Sensitivities. , 3.6.3.4 Functional Characteristics of the APFL on Noisy Signal Processing -- 3.6.3.5 Effect of the APFL on Stochastic Bistable Switch -- 3.6.4 Summary -- 3.7 Exploring Simple Bio-molecular Networks with Specific Functions -- 3.7.1 Motivations -- 3.7.2 Exploring Enzymatic Regulatory Networks with Adaption -- 3.7.2.1 Searching for Circuits Capable of Adaptation -- 3.7.2.2 Identifying Minimal Adaptation Networks -- 3.7.2.3 Key Parameters in Minimal Adaptation Networks -- 3.7.2.4 Negative Feedback Loop with a Buffer Node -- 3.7.2.5 Incoherent FFL with a Proportioner Node -- 3.7.2.6 Exploration of All Possible 3-Node Networks: An NFBLB or IFFLP Architecture is Necessary for Adaptation -- 3.7.2.7 Motif Combinations Can Improve Adaptation -- 3.7.3 Exploring GRNs with Chaotic Behavior -- 3.7.3.1 GRNs and Mathematical Models -- 3.7.3.2 Conditions and Indicators for Chaos -- 3.7.3.3 Main Results -- 3.7.4 Summary -- 3.8 Discussions and Conclusions -- References -- 4 Modeling and Analysis of Coupled Bio-molecular Circuits -- 4.1 Backgrounds -- 4.2 Dynamical Analysis of a Composite Genetic Oscillator -- 4.2.1 Related Works and Motivations -- 4.2.2 Mathematical Models -- 4.2.2.1 The Hysteresis-Based Oscillator -- 4.2.2.2 The Repressilator -- 4.2.2.3 The Composite Oscillator -- 4.2.3 Dynamical Analysis of the Merged Genetic Oscillator -- 4.2.3.1 The Two Oscillatory Mechanisms Support Each Other -- 4.2.3.2 Oscillatory Mechanisms Are Distinct -- 4.2.4 Population Dynamics of Coupled Composite Oscillators -- 4.2.5 Summary -- 4.3 Modeling and Analysis of the Genetic Toggle Switch Circuit -- 4.3.1 Related Works and Motivations -- 4.3.2 Modeling and Analysis of the Single Toggle Switch System -- 4.3.2.1 Deterministic Model -- 4.3.2.2 Bistability -- 4.3.2.3 Stochastic Model for the Single Toggle Switch System -- 4.3.3 Modeling the Networked Toggle Switch Systems. , 4.3.4 Statistical Measurements -- 4.3.5 Stochastic Switch in the Single Toggle Switch System -- 4.3.6 Synchronized Switching in Networked Toggle Switch Systems -- 4.3.6.1 Feature Comparison Between White and Colored Noises Induced Synchronized Switching -- 4.3.6.2 Colored Noise Can Promote the Mean Protein Numbers -- 4.3.6.3 Robustness of Synchronized Switching Against Parameter Perturbations -- 4.3.6.4 Effect of Noise Autocorrelation Time -- 4.3.7 Physical Mechanisms of Bistable Switch -- 4.3.8 Some Further Issues -- 4.3.9 Summary -- 4.4 Discussions and Conclusions -- References -- 5 Modeling and Analysis of Large-Scale Networks -- 5.1 Backgrounds -- 5.2 Continuous Models for the Yeast Cell Cycle Network -- 5.2.1 Related Works and Motivations -- 5.2.2 Dynamical Analysis -- 5.2.3 Summary -- 5.3 Discrete Models for the Yeast Cell Cycle Network -- 5.3.1 Related Works and Motivations -- 5.3.2 Dynamical Analysis -- 5.3.3 Statistical Analysis -- 5.3.3.1 Comparison with Random Networks -- 5.3.3.2 Network Perturbations -- 5.3.4 Summary -- 5.4 Percolating Flow Model for a Mammalian Cellular Network -- 5.4.1 Related Works and Motivations -- 5.4.2 Dynamical Analysis -- 5.4.3 Statistical Analysis -- 5.4.4 Summary -- 5.5 A Hybrid Model for Mammalian Cell Cycle Regulation -- 5.5.1 Related Works and Motivations -- 5.5.2 The Hybrid Model -- 5.5.3 Dynamical Analysis of the Hybrid Model -- 5.5.4 Summary -- 5.6 General Hybrid Model for Large-Scale Bio-Molecular Networks -- 5.6.1 Related Works and Motivations -- 5.6.2 The General Hybrid Model -- 5.6.3 Hybrid Modeling and Analysis of a Toy Genetic Network -- 5.6.3.1 Dynamical Analysis of the Hybrid Model -- 5.6.3.2 Statistical Analysis -- 5.6.4 Summary -- 5.7 Discussions and Conclusions -- References -- Part II Statistical Analysis of Biological Networks -- 6 Evolutionary Mechanisms of Network Motifs in PPI Networks. , 6.1 Backgrounds.

Note: 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. , 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. , 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. , 13 Curiosity and Imitation.

Note: Intro -- Theoretical Foundations of Artificial General Intelligence -- Contents -- 1 Introduction: What Is the Matter Here? -- 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 CognitiveModeling Have the Same Problem -- 2.1 The Intelligence Problem -- 2.1.1 Naming the problem -- 2.1.2 Why the Intelligence Problem is Important -- 2.1.3 The State of the Science -- 2.2 Existing Methods and Standards are not Sufficient -- 2.2.1 Formal linguistics -- 2.2.2 Neuroscience -- 2.2.3 Artificial intelligence -- 2.2.4 Experimental psychology -- 2.3 CognitiveModeling: The Model Fit Imperative -- 2.4 Artificial Intelligence and CognitiveModeling Can Help Each Other -- 2.5 Conclusions -- Bibliography -- 3 Psychometric Artificial General Intelligence: The Piaget-MacGuyver Room -- 3.1 Introduction -- 3.2 More on Psychometric AGI -- 3.2.1 Newell & -- the Neglected Route Toward General Machine Intelligence -- 3.2.2 So, What is Psychometric AGI? -- 3.2.3 Springboard to the Rest of the Present Paper -- 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-likeMind -- 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 -- 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 -- Epilogue. -- Bibliography -- 6 Deep Reinforcement Learning as Foundation for Artificial General Intelligence -- 6.1 Introduction: Decomposing the AGI Problem -- 6.2 Deep Learning Architectures -- 6.2.1 Overcoming the Curse of Dimensionality -- 6.2.2 Spatiotemporal State Inference -- 6.3 Scaling Decision Making under Uncertainty -- 6.3.1 Deep Reinforcement Learning -- 6.3.2 Actor-Critic Reinforcement Learning Themes in Cognitive Science -- 6.4 Neuromorphic Devices Scaling AGI -- 6.5 Conclusions and Outlook -- Bibliography -- 7 The LIDA Model as a Foundational Architecture for AGI -- 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 -- 7.4.1 7.4.1 Ron Sun's Desiderata [53 -- 7.4.2 Newell's functional criteria (adapted from Lebiere and Anderson 2003) -- 7.4.3 BICA table -- 7.5 Discussion, Conclusions -- Bibliography -- 8 The Architecture of Human-Like General Intelligence -- 8.1 Introduction -- 8.2 Key Ingredients of the Integrative Human-Like Cognitive Architecture Diagram -- 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: From Manual Methods to Self-Constructive Systems -- 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 -- 9.5.1 Temporal Grounding -- 9.5.2 Feedback Loops -- 9.5.3 Pan-Architectural Pattern Matching -- 9.5.4 Transparent Operational Semantics -- 9.5.5 Integration and Architecture Metaconstruction -- 9.6 Conclusions. , Acknowledgments -- Bibliography -- 10 Towards an Actual Gödel Machine Implementation: A Lesson in Self-Reflective Systems -- 10.1 Introduction -- 10.2 The Gödel Machine Concept -- 10.3 The Theoretical Foundations of Self-Reflective Systems -- 10.3.1 Basic λ -calculus -- 10.3.2 Constants, Conditionals, Side-effects, and Quoting -- 10.4 Nested Meta-Circular Evaluators -- 10.5 A Functional Self-Reflective System -- 10.6 Discussion -- Bibliography -- 11 Artificial General Intelligence Begins with Recognition: Evaluating the Flexibility of Recognition -- 11.1 Introduction -- 11.2 Evaluating Flexibility -- 11.2.1 The Testing Paradigm -- 11.2.2 Combinatorial Difficulties of Superposition or Mixes -- 11.2.3 "Occluding" Superpositions -- 11.2.4 Counting Tests -- 11.2.5 Binding Tests -- 11.2.6 Binding and The Set-Cover Problem -- 11.2.7 Noise Tests -- 11.2.8 Scoring the Tests -- 11.2.9 Evaluating Algorithms' Resources -- 11.3 Evaluation of Flexibility -- 11.3.1 Superposition Tests with Information Loss -- 11.3.2 Superpositions without loss -- 11.3.3 Counting Tests -- 11.3.4 Binding Scenarios -- 11.3.5 Noise Tests -- 11.3.6 Scoring Tests Together -- 11.3.7 Conclusion from Tests -- 11.4 Summary -- Acknowledgments -- Bibliography -- 12 Theory Blending as a Framework for Creativity in Systems for General Intelligence -- 12.1 Introduction -- 12.2 Productivity and CognitiveMechanisms -- 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 Motivation and the Emergence of Affect in a Cognitive Agent -- 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 -- Acknowledgments -- Bibliography -- 14 AGI and Machine Consciousness -- 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 FreeWill -- 14.8 Experience -- 14.9 Creativity -- 14.10 Conclusions -- Bibliography -- 15 Human and Machine Consciousness as a Boundary Effect in the Concept Analysis Mechanism -- 15.1 Introduction -- 15.1.1 The Hard Problem of Consciousness -- 15.1.2 A Problem within the Hard Problem -- 15.1.3 An Outline of the Solution -- 15.2 The Nature of Explanation -- 15.2.1 The Analysis Mechanism -- 15.2.2 The Molecular Framework -- 15.2.3 Explanation in General -- 15.2.4 Explaining Subjective Concepts -- 15.2.5 The "That Misses The Point" Objection -- 15.3 The Real Meaning of Meaning -- 15.3.1 Getting to the Bottom of Semantics -- 15.3.2 Extreme Cognitive Semantics -- 15.3.3 Implications -- 15.4 Some Falsifiable Predictions -- 15.4.1 Prediction 1: Blindsight -- 15.4.2 Prediction 2: New Qualia -- 15.4.3 Prediction 3: Synaesthetic Qualia -- 15.4.4 Prediction 4: Mind Melds -- 15.5 Conclusion -- Bibliography -- 16 Theories of Artificial Intelligence -Meta-theoretical considerations -- 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 -- Acknowledgements -- Bibliography -- Index.

Note: Intro -- Preface -- Editorial Committee -- Editors -- Honorary Editor -- Steering Committee -- Members -- Contents -- Fuzzy Information Processing -- (in, inveeq(λ,μ))-Fuzzy Weak Ideal of Complemented Semirings -- 1 Introduction -- 2 Preliminaries -- 3 (in, inveeq(λ,μ))-Fuzzy Completely Prime Ideals -- 4 Conclusion -- References -- Generalized Fuzzy Sets and Fuzzy Relations -- 1 Introduction -- 2 Generalized Fuzzy Sets and Their Operations -- 3 Generalized Fuzzy Relations -- References -- Fuzzy Topologies and Fuzzy Preorders Based on Complete Co-residuated Lattices -- 1 Introduction -- 2 Preliminaries -- 3 Fuzzy Topologies and Fuzzy Orders Valued by a Complete Co-residuated Lattice -- 4 Conclusion -- References -- 4 A Classification Method of Fuzzy Sets Based on Rough Fuzzy Number -- Abstract -- 1 Introduction -- 2 Rough Fuzzy Number -- 3 Classification Method of Fuzzy Sets and Classification Judgment Function -- 4 Conclusion -- Acknowledgement -- References -- A Prediction Model for Hot Metal Desulfurization Rate Based on Fuzzy Structured Element -- 1 Introduction -- 2 Preliminaries -- 2.1 Fuzzy Structure Element and Its Expression -- 2.2 The Fuzzy Multiple Linear Regression Model -- 3 Fuzzy Multivariate Linear Regression Model of Desulfurization Rate -- 3.1 Problem Description and Model Establishment -- 3.2 Solution of the Model -- 4 Calculation Examples -- 4.1 Establishment and Calculation of Model -- 4.2 The Prediction and Test of Model -- 5 Conclusions -- References -- Fuzzy Reasoning Triple I Constraint Method Based on Family of Implication Operator L-λ-Π -- 1 Introduction -- 2 Preliminaries -- 3 The α-Triple I Constraint Method for FMP -- 4 The α-Triple I Constraint Method for FMT -- 5 Conclusion -- References -- Fuzzy Information Fusion Approach for Supplier Selection -- 1 Introduction -- 2 Definitions and Properties of GFNs. , 3 Nonlinear Aggregation Operators with GFNs -- 4 Ranking Formula in GDM Based on Mean Values of GFNs -- 5 Numerical Example -- 6 Conclusions -- References -- Possibility-Based Outranking Comparison for PROMETHEE II with Uncertain Linguistic Fuzzy Variables -- 1 Introduction -- 2 Preliminaries -- 3 Possibility-Based Comparison for PROMETHEE II Model -- 3.1 Uncertain Linguistic Decision System -- 3.2 The Overall Dominance Possibility Assignment Methods -- 3.3 Dominance Possibility Preference Functions for PROMETHEE II Model -- 3.4 Steps of Proposed Model -- 4 Illustrative Applications -- 5 Conclusion -- References -- 9 Hesitant Fuzzy Correlation Measures Considering the Credibility -- Abstract -- 1 Introduction -- 2 Basic Theory of Hesitant Fuzzy Sets -- 3 Determination Method of the Attribute Weight Based on Correlation Degree -- 3.1 The Correlation Degree of the Hesitant Fuzzy Set and Its Properties Considering the Credibility -- 3.2 Determination of the Attribute Weight -- 4 Concluding Remarks -- References -- Pan-uncertain Measure -- 1 Introduction -- 2 Preliminary -- 3 Pan-uncertain Variables -- 4 Conclusion -- References -- Fuzzy Risk Analysis Method Based on Trapezoidal Intuitionistic Fuzzy Numbers -- 1 Introduction -- 2 Preliminaries -- 3 Weighted Similarity Measure Between Trapezoidal Intuitionistic Fuzzy Numbers -- 4 Fuzzy Risk Analysis Based on the Similarity Measure Between Intuitionistic Fuzzy Numbers -- 5 Conclusion -- References -- On Fuzzy Soft Relation -- 1 Introduction -- 2 Preliminaries -- 3 Soft Relation and Fuzzy Soft Relation -- 4 Conclusion -- References -- 13 Fire Detection in Video Using Fuzzy Pattern Recognition -- Abstract -- 1 Introduction -- 2 Proposed Fire Detection Algorithm -- 2.1 Moving Region Detection Using Background Subtraction -- 2.2 Segmentation of Suspected Region Using Fire Color Model. , 2.3 Parameters Extraction -- 2.3.1 Jumping Frequency -- 2.3.2 Circularity Feature -- 2.3.3 Rectangle Filling Coefficient -- 2.3.4 The Sharp Angle Feature of Flame -- 2.4 Flame Alarm Decision Using a Fuzzy Classifier -- 3 Experimental Results and Analysis -- 4 Conclusion -- Acknowledgements -- References -- 14 Fuzzy Prediction in Classification of AdaBoost Algorithm -- Abstract -- 1 Introduction to AdaBoost -- 2 Fuzzy Mathematics -- 2.1 Sets -- 2.2 Fuzzy Subsets and Membership Function -- 2.3 Fuzzy Mathematical Model -- 3 Fuzzy AdaBoost -- 3.1 Training -- 3.2 Predictions -- 4 Data Experiment -- 5 Conclusion -- References -- Einstein Choquet Integral Operators for PROMETHEE II Group Decision Making Method with Triangular Intuitionistic Fuzzy Numbers -- 1 Introduction -- 2 Preliminaries -- 2.1 Triangular Intuitionistic Fuzzy Numbers -- 2.2 Einstein Operations -- 2.3 Einstein Operations of Triangular Intuitionistic Fuzzy Numbers -- 2.4 Ratio Ranking Method of Triangular Intuitionistic Fuzzy Numbers -- 2.5 Einstein Geometric Aggregation Operators for TIFNs -- 3 Triangular Intuitionistic Fuzzy Einstein Choquet Geometric Operator -- 4 PROMETHEE II Based on TIFECGμ Operator -- 5 A Numerical Example -- 6 Conclusion -- References -- 16 Dynamics Analysis and Fuzzy Control for the Working Device of Hydraulic Excavator -- Abstract -- 1 Introduction -- 2 Dynamic Modeling of the Working Device Based on Kane-Huston Equations -- 3 Virtual Prototype Simulation of Working Device Based on the ADAMS -- 3.1 Virtual Prototype Simulation -- 3.2 Inverse Dynamic Problem from Virtual Prototype Simulation and Kane-Huston Equation -- 4 The Fuzzy Control Simulation of Virtual Prototype of Working Device -- 5 Conclusion -- References -- Fuzzy Engineering -- 17 The Application and Predictive Models Base on Bayesian Classifier in Electronic Information Industry -- Abstract. , 1 Introduction -- 2 Preparation for Modeling -- 2.1 Assumed Condition -- 2.2 Index System and Data Preprocessing -- 2.2.1 Constitute the Index System -- 2.2.2 Data Preprocessing -- 2.3 Model Establishment -- 3 Text Clustering Algorithm Based on Factors Space -- 3.1 The Establishment of the Electronic Information Industry Development Index Model -- 3.2 Time Series Forecasting Model -- 3.3 Bayesian Classifier Model -- 4 Model Solution -- 4.1 Calculation and Forecast of the Development α Index of the Electronic Information Industry in the Provinces -- 4.2 Bayesian Hierarchical System -- 5 Conclusion -- Acknowledgements -- References -- 18 Fuzzy Formulation of the Lee-Carter Model for the Mortality Forecasting with Age-Specific Enhancement -- Abstract -- 1 Introduction -- 2 Review of the Lee-Carter Model and Fuzzy Set Theory -- 3 Fuzzy Formulation of the LC Model with Age-Specific Enhancement -- 4 Mortality Forecasting: China Data -- 5 Conclusions and Remarks: China Data -- References -- Interval Number Comparison and Decision Making Based on Priority Degree -- 1 Introduction -- 2 Priority Degree and Interval Number Ranking -- 2.1 Existing Possibility Degrees and Their Deficiencies -- 2.2 Priority Degree and the Interval Number Ranking -- 3 The Comparison of Interval Numbers -- 4 The Reduction for Addition and Subtraction -- 5 Priority-Degree Based Multi-criteria Decision Making -- 6 Conclusion -- References -- 20 T-Absolute Truth Degree Theory of Formulas in Three-Valued Łukasiewicz Propositional Logic System -- Abstract -- 1 Introduction -- 2 T-Absolute Truth Degree Theory of Formulas -- 3 T-Absolute Similarity Degree and Pseudo-Distance Between Two Formulas -- 4 Conclusion -- References -- Universal Function Projective Synchronization of Chaotic Systems with Uncertainty by Using Active Fuzzy Sliding Mode Control -- 1 Introduction. , 2 Definition of UFPS -- 3 System Description and Problem Formulation -- 4 Active Fuzzy Sliding Mode Control -- 5 Numerical Results and Analysis -- 6 Conclusion -- References -- 22 Hesitant Fuzzy Prioritized Hybrid Average Operator and Its Application to Multiple Attribute Decision Making -- Abstract -- 1 Introduction -- 2 Fundamental Theory of Hesitant Fuzzy Set -- 3 Hesitant Fuzzy Prioritized Weighted Method -- 3.1 Hesitant Fuzzy Information Entropy -- 3.2 A Prioritized Hybrid Weighted Method Based on the Hesitant Fuzzy Information Entropy -- 4 Hesitant Fuzzy Prioritized Hybrid Average (HFPHA) Operator -- 5 Personnel Evaluation Based on the HFPHA Operator -- 6 Conclusion -- References -- 23 Differential Transform Method for Solving Linear System of First-Order Fuzzy Differential Equations -- Abstract -- 1 Introduction -- 2 Preliminaries -- 3 Differential Transform Method -- 4 Example -- 5 Conclusion -- Acknowledgments -- References -- Weight of Basic Health Service Equalization Index Based on the Intuitionistic Fuzzy Analytic Hierarchy Process -- 1 Introduction -- 2 Consistency Adjustment Algorithm of Intuitionistic Fuzzy Complementary Judgment Matrix -- 2.1 IFCJM and Its Properties -- 2.2 The Consistency Adjustment Algorithm -- 3 Analytic Hierarchy Process Under the Intuitionistic Fuzzy Environment and Its Application -- 3.1 Intuitionistic Fuzzy Analytic Hierarchy Process -- 3.2 The Evaluation of Basic Health Service Equalization -- 4 Conclusion -- References -- 25 About Approach to Multi-attribute Decision Making Problems Based on COWA Operator Under Interval-Valued Intuitionistic Fuzzy Environment -- Abstract -- 1 Introduction -- 2 Preliminaries -- 3 New Hamming Distance and Entropy Based on COWA Operator for IVIFSs -- 3.1 Generalized Hamming Distance for IVIFS -- 3.2 Generalized Entropy for IVIFS. , 4 An Approach to Multi-attribute Fuzzy Decision Making.