Table of contents for Artificial intelligence with uncertainty / Deyi Li and Yi Du.

Bibliographic record and links to related information available from the Library of Congress catalog.

Note: Contents data are machine generated based on pre-publication provided by the publisher. Contents may have variations from the printed book or be incomplete or contain other coding.


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Chapter 1 The 50 - year History of Artificial Intelligence	1
1.1 Departure from Dartmouth Symposium	1
1.1.1 Communications between Different Disciplines	1
1.1.2 Development and Growth	3
1.2 Expected Goals as Time Goes on	4
1.2.1 Turing Test	4
1.2.2 Machine Theorem Proof	5
1.2.3 Rivalry between Kasparov and Deep Blue	6
1.2.4 Thinking Machine	7
1.2.5 Artificial Life	8
1.3 AI Achievements in 50 years	9
1.3.1 Pattern Recognition	10
1.3.2 Knowledge Engineering	12
1.3.3 Robotics	14
1.4 Major Development of AI in the Information Age	15
1.4.1 Impacts of AI Technology on the Whole Society	15
1.4.2 From World Wide Web to Intelligent Grid	16
1.4.3 From Data to Knowledge	17
1.5 The Cross Trend between AI, Brain Science and Cognitive Science	18
1.5.1 The Influence of Brain Science to AI	18
1.5.2 The Influence of Cognitive Science to AI	21
1.5.3 Coming Breakthroughs Caused by Interdisciplines	22
Chapter 2 Methodologies of AI	1
2.1 Symbolism Methodology	1
2.1.1 Birth and Development of Symbolism	1
2.1.2 Predicate Calculus and Resolution Principle	5
2.1.3 Logic Programming Language	7
2.1.4 Expert System	9
2.2 Connectionism Methodology	11
2.2.1 Birth and Development of Connectionism	12
2.2.2 Strategy and Technical Characteristics of Connectionism	13
2.2.3 Hopfield Neural Network Model	16
2.2.4 Back-Propagation Neural Network Model	17
2.3 Behaviorism Methodology	19
2.3.1 Birth and Development of Behaviorism	19
2.3.2 Robot Control	20
2.3.3 Intelligent Control	21
2.4 Reflection on Methodologies	23
Chapter 3 On Uncertainties of Knowledge	1
3.1 On Randomness	1
3.1.1 The Objectivity of Randomness	1
3.1.2 The Beauty of Randomness	4
3.2 On Fuzziness	6
3.2.1 The Objectivity of Fuzziness	7
3.2.2 The Beauty of Fuzziness	9
3.3 Uncertainties in Natural Languages	11
3.3.1 Languages as the Carrier of Human's Knowledge	11
3.3.2 Uncertainties in Languages	12
3.4 Uncertainties in Commonsense Knowledge	15
3.4.1 Common Understanding about Common Sense	15
3.4.2 Relativity of Commonsense Knowledge	17
3.5 Other Uncertainties of Knowledge	18
3.5.1 Incompleteness of Knowledge	19
3.5.2 Incoordination of Knowledge	20
3.5.3 Impermanence of Knowledge	21
Chapter 4 Mathematical Foundation of AI with Uncertainty	1
4.1 Probability Theory	1
4.1.1 Bayes Theorem	2
4.1.2 Probability Distribution Function	6
4.1.3 Normal Distribution	8
4.1.4 Large Number Laws and Central Limit Theorem	11
4.1.5 Power Law Distribution	14
4.1.6 Entropy	16
4.2 Fuzzy Set Theory	17
4.2.1 Membership Degree and Membership Function	18
4.2.2 Decomposition Theorem and Expanded Principle	20
4.2.3 Fuzzy Relation	22
4.2.4 Possibility Measure	23
4.3 Rough Set Theory	24
4.3.1 Imprecise Category and Rough Set	25
4.3.2 Characteristics of Rough Set	28
4.3.3 Rough Relations	29
4.4 Chaos and Fractal	32
4.4.1 Basic Characteristics of Chaos	33
4.4.2 Strange Attractors of Chaos	36
4.4.3 Geometric Characteristics of Chaos and Fractal	38
4.5 Kernel Functions and Principal Curves	38
4.5.1 Kernel Functions	39
4.5.2 Support Vector Machine	42
4.5.3 Principal Curves	45
Chapter 5 Qualitative and Quantitative Transform Model - Cloud Model	1
5.1 Perspectives in the Study of AI with Uncertainty	1
5.1.1 Multiple Perspectives in the Study of Human¿s Intelligence	1
5.1.2 Importance of Concepts in Natural Languages	4
5.1.3 Relationship between Randomness and Fuzziness in a Concept	5
5.2 Representing Concepts Using Cloud Models	7
5.2.1 Cloud and Cloud Drop	7
5.2.2 Numerical Characteristics of Cloud	9
5.2.3 Types of Cloud Model	10
5.3 Normal Cloud Generator	11
5.3.1 Forward Cloud Generator	11
5.3.2 Contributions of Cloud Drops to a Concept	16
5.3.3 Understanding Lunar Calendar¿s Solar Terms through Cloud Models	17
5.3.4 Backward Cloud Generator	18
5.3.5 Precision Analysis of Backward Cloud Generator	24
5.3.6 More on understanding Normal Cloud Model	26
5.4 Mathematical Properties of Normal Cloud	29
5.4.1 Statistical Analysis of the Cloud Drops¿ Distribution	29
5.4.2 Statistical Analysis of the Cloud Drops¿ Certainty Degree	31
5.4.3 Expectation Curves of Normal Cloud	33
5.5 On the Pervasiveness of the Normal Cloud Model	34
5.5.1 Pervasiveness of Normal Distribution	35
5.5.2 Pervasiveness of Bell Membership Function	36
5.5.3 Significance of Normal Cloud	38
Chapter 6 Discovering Knowledge with Uncertainty through Methodologies in Physics	1
6.1 From Perception of Physical World to Perception of Human Self	1
6.1.1 Expressing Concepts by Using Atom Models	2
6.1.2 Describing Interaction between Objects by Using Field	3
6.1.3 Describing Hierarchical Structure of Knowledge by Using Granularity	5
6.2 Data Field	7
6.2.1 From Physical Field to Data Field	7
6.2.2 Potential Field and Force Field of Data	10
6.2.3 Influence Coefficient Optimization of Field Function	20
6.2.4 Data Field and Visual Thinking Simulation	23
6.3 Uncertainty in Concept Hierarchy	28
6.3.1 Discretization of Continuous Data	30
6.3.2 Virtual Pan Concept Tree	36
6.3.3 Cross-Layer Strategy and Algorithms	37
6.4 Knowledge Discovery State Space	46
6.4.1 Three Kinds of State Spaces	47
6.4.2 State Space Transformation	48
6.4.3 Major Operations in State Space Transformation	50
Chapter 7 Data Mining for Discovering Knowledge with Uncertainty	1
7.1 Uncertainty in Data Mining	1
7.1.1 Data Mining and Knowledge Discovery	1
7.1.2 Uncertainty in Data Mining Process	3
7.1.3 Uncertainty in Discovered Knowledge	5
7.2 Classification and Clustering with Uncertainty	6
7.2.1 Cloud Classification	7
7.2.2 Clustering Based on Data Field	15
7.2.3 Examination and Discovery of Offlets Based on Data Field	41
7.3 Discovery of Association Rules with Uncertainty	47
7.3.1 Reconsideration on the Traditional Association Rules	47
7.3.2 Association Rule Mining and Forecasting	51
7.4 Time Series Data Mining and Forecasting	58
7.4.1 Time Series Data Mining Based on Cloud Models	60
7.4.2 Stock Data Forecasting	62
Chapter 8 Reasoning and Control of Qualitative Knowledge	1
8.1 Qualitative Rule Construction by Cloud	1
8.1.1 Precondition Cloud Generator and Postcondition Cloud Generator	2
8.1.2 Rule Generator	4
8.1.3 From Cases to Rule Generation	8
8.2 Qualitative Control Mechanism	9
8.2.1 Fuzzy, Probability, and Cloud Control Methods	9
8.2.2 Theoretic Explanation of Mamdani Fuzzy Control Method	19
8.3 Inverted Pendulum - an Example of Intelligent Control with Uncertainty	21
8.3.1 Inverted Pendulum System and Its Control	21
8.3.2 Inverted Pendulum Qualitative Control Mechanism	22
8.3.3 Cloud Control Policy of Triple Link Inverted Pendulum	25
8.3.4 Balancing Patterns of an Inverted Pendulum	36
Chapter 9 A New Direction of AI with Uncertainty	1
9.1 Computing with Words	2
9.2 Study on Cognitive Physics	7
9.2.1 Extension of Cloud Model	7
9.2.2 Dynamic Data Field	12
9.3 Complex Networks with Small World and Scale-Free Models	15
9.3.1 Regularity of Uncertainty in Complex Network	16
9.3.2 Scale-Free Networks Generation	20
9.3.3 Applications of Data Field Theory to Networked Intelligence	24
9.4 Long Way to Go for AI with Uncertainty	25
9.4.1 Limitations of Cognitive Physics Methodology	25
9.4.2 Divergences from Daniel, the Nobel Economic Prize Winner	26

Library of Congress Subject Headings for this publication:

Artificial intelligence.
Uncertainty (Information theory).