(Ebook PDF) Artificial Intelligence with Uncertainty 2nd Edition by Deyi Li, Yi Du ISBN 9781315349831 1315349833 full chapters

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ISBN 10:1315349833

ISBN 13:9781315349831

Author: Deyi Li, Yi Du

Table of Contents:

• 1 Artificial Intelligence Challenged by Uncertainty
• 1.1 The Uncertainty of Human Intelligence
• 1.1.1 The Charm of Uncertainty
• 1.1.2 The World of Entropy
• 1.2 Sixty Years of Artificial Intelligence Development
• 1.2.1 The Dartmouth Symposium
• 1.2.1.1 Collision between Different Disciplines
• 1.2.1.2 Ups and Downs in Development
• 1.2.2 Goals Evolve over Time
• 1.2.2.1 Turing Test
• 1.2.2.2 Proving Theorems by Machine
• 1.2.2.3 Rivalry between Kasparov and Deep Blue
• 1.2.2.4 Thinking Machine
• 1.2.2.5 Artificial Life
• 1.2.3 Significant Achievements in AI over the Past 60 Years
• 1.3 Research Methods for AI
• 1.3.1 Symbolism
• 1.3.2 Connectionism
• 1.3.3 Behaviorism
• 1.4 Interdisciplinary Trends in AI
• 1.4.1 Brain Science and AI
• 1.4.2 Cognitive Science and AI
• 1.4.3 Network Science and AI
• 1.4.4 Great Breakthroughs to Be Achieved by Interdisciplinary Research
• References
• 2 Cloud Model: A Cognitive Model for Qualitative and Quantitative Transformation
• 2.1 Starting Points for the Study of Artificial Intelligence with Uncertainty
• 2.1.1 Multiple Starting Points
• 2.1.2 Keeping in Mind Concepts of Natural Languages
• 2.1.3 Randomness and Fuzziness in Concepts
• 2.2 Using Cloud Models to Represent Uncertainties of Concepts
• 2.2.1 Cloud and Cloud Drops
• 2.2.2 The Cloud’s Digital Characteristics
• 2.2.3 Various Types of Cloud Models
• 2.3 Algorithm of Forward Gaussian Cloud
• 2.3.1 Description
• 2.3.1.1 Forward Gaussian Cloud Algorithm
• 2.3.1.2 Two-Dimensional Forward Gaussian Algorithm
• 2.3.2 Contributions Made by Cloud Drops to the Concept
• 2.3.3 Using Gaussian Cloud Model to Understand the 24 Solar Terms in the Chinese Lunar Calendar
• 2.4 Mathematical Properties of the Gaussian Cloud
• 2.4.1 Statistical Analysis of the Distribution of Cloud Drops
• 2.4.2 Statistical Analysis of Certainty Degree of Cloud Drops
• 2.4.3 Expectation Curves of Gaussian Cloud
• 2.4.4 From Cloud to Fog
• 2.5 Algorithm of Backward Gaussian Cloud
• 2.5.1 Description
• 2.5.1.1 Backward Gaussian Cloud Algorithm with Certainty Degree
• 2.5.1.2 Backward Cloud Algorithm Based on the First-Order Absolute Central Moment and the Second-Order Central Moment
• 2.5.1.3 Backward Cloud Algorithm Based on the Second- and Fourth-Order Central Moments of Samples
• 2.5.2 Parameter Estimation and Error Analysis of Backward Gaussian Cloud
• 2.5.2.1 Error Analysis of Ex
• 2.5.2.2 Error Analysis of En and He
• 2.5.2.3 Determining the Number of Samples under the Condition of Given Errors and Confidence Level
• 2.6 Further Understanding of the Cloud Model
• 2.6.1 Judgment Shooting
• 2.6.2 Fractal with Uncertainty
• 2.7 Universality of the Gaussian Cloud
• 2.7.1 Universality of Gaussian Distribution
• 2.7.2 Universality of Bell-Shaped Membership Function
• 2.7.3 Universal Relevance of Gaussian Cloud
• References
• 3 Gaussian Cloud Transformation
• 3.1 Terminology in Granular Computing
• 3.1.1 Scale, Level, and Granularity
• 3.1.2 Concept Tree and Pan-Concept Tree
• 3.2 Gaussian Transformation
• 3.2.1 Parameter Estimation of Gaussian Transform
• 3.2.2 Gaussian Transform Algorithm
• 3.3 Gaussian Cloud Transformation
• 3.3.1 From Gaussian Transformation to Gaussian Cloud Transformation
• 3.3.2 Heuristic Gaussian Cloud Transformation
• 3.3.3 Adaptive Gaussian Cloud Transformation
• 3.3.4 High-Dimensional Gaussian Cloud Transformation
• 3.4 Gaussian Cloud Transformation for Image Segmentation
• 3.4.1 Detection of the Transition Zone in Images
• 3.4.2 Differential Object Extraction in Image
• References
• 4 Data Fields and Topological Potential
• 4.1 Data Field
• 4.1.1 Using Field to Describe Interactive Objects
• 4.1.2 From Physical Field to Data Field
• 4.1.3 Potential Field and Force Field of Data
• 4.1.4 Selection of Influence Coefficient in Field Function
• 4.2 Clustering Based on Data Field
• 4.2.1 Uncertainty in Classification and Clustering
• 4.2.2 Dynamic Clustering Based on Data Field
• 4.2.2.1 Selecting Representative Objects through Mass Estimation
• 4.2.2.2 Initial Clustering of Data Samples
• 4.2.2.3 Dynamic Clustering of Representative Objects
• 4.2.3 Expression Clustering of Human Face Images Based on Data Field
• 4.2.3.1 Feature Extraction Based on Face Image Data Field
• 4.2.3.2 Recognition of Facial Expression Cluster Based on K–L Transformation and Second- Order Data Field
• 4.3 Complex Network Research Based on Topological Potential
• 4.3.1 From Data Field to Topological Potential
• 4.3.2 Important Network Nodes Detected with Topological Potential
• 4.3.3 Network Community Discovery Based on Topological Potential
• 4.3.4 Hot Entries in Wikipedia Discovered with Topological Potential
• References
• 5 Reasoning and Control of Qualitative Knowledge
• 5.1 Cloud Reasoning
• 5.1.1 Using a Cloud Model to Construct Qualitative Rules
• 5.1.1.1 One-Dimensional Precondition Cloud Generator
• 5.1.1.2 Postcondition Cloud Generator
• 5.1.1.3 Single-Condition-Single-Rule Generator
• 5.1.2 Generation of Rule Sets
• 5.2 Cloud Control
• 5.2.1 Mechanism of Cloud Control
• 5.2.2 Theoretical Explanation of the Mamdani Fuzzy Control Method
• 5.3 Uncertainty Control in Inverted Pendulum
• 5.3.1 Inverted Pendulum System and Its Control
• 5.3.2 Qualitative Control Mechanisms for Single-Link/Double-Link Inverted Pendulums
• 5.3.3 Cloud Control Strategy for a Triple-Link Inverted Pendulum
• 5.3.3.1 Qualitative Analysis of the Triple-Link Inverted Pendulum System
• 5.3.3.2 The Cloud Controller of the Triple-Link Inverted Pendulum System
• 5.3.4 Balancing Patterns of the Inverted Pendulum
• 5.3.4.1 Balancing Patterns of the Single-Link Inverted Pendulum
• 5.3.4.2 Balancing Patterns of the Double-Link Inverted Pendulum
• 5.3.4.3 Balancing Patterns of the Triple-Link Inverted Pendulum
• 5.4 Uncertainty Control in Intelligent Driving
• 5.4.1 Intelligent Driving of Automobiles
• 5.4.1.1 Integration of the Right of Way Radar Map
• 5.4.1.2 Cloud Control Strategy of Intelligent Vehicle
• 5.4.2 Driving Behavior Simulation Based on Intelligent Automobiles
• References
• 6 Cognitive Physics for Swarm Intelligence
• 6.1 Interaction: The Important Cause of Swarm Intelligence
• 6.1.1 Swarm Intelligence
• 6.1.2 Emergence as a Form to Represent Group Behavior
• 6.2 Application of Cloud Model and Data Field in Swarm Intelligence
• 6.2.1 Cloud Model to Represent Discrete Individual Behavior
• 6.2.2 Data Field to Describe Interactions between Individuals
• 6.3 Typical Case: “Applause Sounded”
• 6.3.1 Cloud Model to Represent People’s Applauding Behavior
• 6.3.1.1 Simplification and Modeling of Individual Behavior
• 6.3.1.2 Simplification and Modeling of the Environment
• 6.3.1.3 Initial Distribution and Presentation of Individual Behavior
• 6.3.2 Data Field to Reflect Mutual Spread of Applause
• 6.3.3 Computing Model for “Applause Sounded”
• 6.3.4 Experimental Platform
• 6.3.5 Diversity Analysis of Emergence
• 6.3.6 Guided Applause Synchronization
• References
• 7 Great Development of Artificial Intelligence with Uncertainty due to Cloud Computing
• 7.1 An Insight into the Contributions and Limitations of Fuzzy Set from the Perspective of a Cloud Model
• 7.1.1 Paradoxical Argument over Fuzzy Logic
• 7.1.2 Dependence of Fuzziness on Randomness
• 7.1.3 From Fuzzy to Uncertainty Reasoning
• 7.2 From Turing Computing to Cloud Computing
• 7.2.1 Cloud Computing beyond the Turing Machine
• 7.2.2 Cloud Computing and Cloud Model
• 7.2.3 Cloud Model Walking between Gaussian and Power Law Distribution
• 7.3 Big Data Calls for AI with Uncertainties
• 7.3.1 From Database to Big Data
• 7.3.2 Network Interaction and Swarm Intelligence
• 7.4 Prospect of AI with Uncertainty

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