Contents -- Preface -- Acknowledgement -- Chapter 1 Introduction -- 1.1 Brief History of AI -- 1.2 Cognitive Issues of AI -- 1.3 Hierarchical Model of Thought -- 1.4 Symbolic Intelligence -- 1.5 Research Approaches of Artificial Intelligence -- 1.5.1 Cognitive School -- 1.5.2 Logical School -- 1.5.3 Behavioral School -- 1.6 Automated Reasoning -- 1.7 Machine Learning -- 1.8 Distributed Artificial Intelligence -- 1.9 Artificial Thought Model -- 1.10 Knowledge Based Systems -- Exercises -- Chapter 2 Logic Foundation of Artificial Intelligence -- 2.1 Introduction -- 2.2 Logic Programming -- 2.2.1 Definitions of logic programming -- 2.2.2 Data structure and recursion in Prolog -- 2.2.3 SLD resolution -- 2.2.4 Non-logic components: CUT -- 2.3 Nonmonotonic Logic -- 2.4 Closed World Assumption -- 2.5 Default Logic -- 2.6 Circumscription Logic -- 2.7 Nonmonotonic Logic NML -- 2.8 Autoepistemic Logic -- 2.8.1 Moore System LB -- 2.8.2 OL Logic -- 2.8.3 Theorems on normal forms -- 2.8.4 ◇- mark and a kind of course of judging for stable expansion -- 2.9 Truth Maintenance System -- 2.10 Situation Calculus -- 2.10.1 Many-sorted logic for situation calculus -- 2.10.2 Basic action theory in LR -- 2.11 Frame Problem -- 2.11.1 Frame Axiom -- 2.11.2 Criteria for a solution to the frame problem -- 2.11.3 Nonmonotonic solving approach of the frame problem -- 2.12 Dynamic Description Logic -- 2.12.1 Description Logic -- 2.12.2 Syntax of dynamic description logic -- 2.12.3 Semantics of dynamic description logic -- Exercises -- Chapter 3 Constraint Reasoning -- 3.1 Introduction -- 3.2 Backtracking -- 3.3 Constraint Propagation -- 3.4 Constraint Propagation in Tree Search -- 3.5 Intelligent Backtracking and Truth Maintenance -- 3.6 Variable Instantiation Ordering and Assignment Ordering -- 3.7 Local Revision Search -- 3.8 Graph-based Backjumping.

3.9 Influence-based Backjumping -- 3.10 Constraint Relation Processing -- 3.10.1 Unit Sharing Strategy for Identical Relation -- 3.10.2 Interval Propagation -- 3.10.3 Inequality Graph -- 3.10.4 Inequality Reasoning -- 3.11 Constraint Reasoning System COPS -- 3.12 ILOG Solver -- Exercise -- Chapter 4 Qualitative Reasoning -- 4.1 Introduction -- 4.2 Basic approaches in qualitative reasoning -- 4.3 Qualitative Model -- 4.4 Qualitative Process -- 4.5 Qualitative Simulation Reasoning -- 4.5.1 Qualitative state transformation -- 4.5.2 QSIM algorithm -- 4.6 Algebra Approach -- 4.7 Spatial Geometric Qualitative Reasoning -- 4.7.1 Spatial logic -- 4.7.2 Temporal spatial relation -- 4.7.3. Applications of temporal and spatial logic -- 4.7.4. Randell algorithm -- Exercises -- Chapter 5 Case-Based Reasoning -- 5.1 Overview -- 5.2 Basic Notations -- 5.3 Process Model -- 5.4 Case Representation -- 5.4.1 Semantic Memory Unit -- 5.4.2 Memory Network -- 5.5 Case Indexing -- 5.6 Case Retrieval -- 5.7 Similarity Relations in CBR -- 5.7.1 Semantic similarity -- 5.7.2 Structural similarity -- 5.7.3 Goal's features -- 5.7.4 Individual similarity -- 5.7.5 Similarity assessment -- 5.8 Case Reuse -- 5.9 Case Retainion -- 5.10 Instance-Based Learning -- 5.10.1 Learning tasks of IBL -- 5.10.2 Algorithm IB1 -- 5.10.3 Reducing storage requirements -- 5.11 Forecast System for Central Fishing Ground -- 5.11.1 Problem Analysis and Case Representation -- 5.11.2 Similarity Measurement -- 5.11.3 Indexing and Retrieval -- 5.11.4 Revision with Frame -- 5.11.5 Experiments -- Exercises -- Chapter 6 Probabilistic Reasoning -- 6.1 Introduction -- 6.1.1 History of Bayesian theory -- 6.1.2 Basic concepts of Bayesian method -- 6.1.3 Applications of Bayesian network in data mining -- 6.2 Foundation of Bayesian Probability -- 6.2.1 Foundation of probability theory -- 6.2.2 Bayesian probability.

6.3 Bayesian Problem Solving -- 6.3.1 Common methods for prior distribution selection -- 6.3.2 Computational learning -- 6.3.3 Steps of Bayesian problem solving -- 6.4 Naïve Bayesian Learning Model -- 6.4.1 Naïve Bayesian learning model -- 6.4.2 Boosting of naïve Bayesian model -- 6.4.3 The computational complexity -- 6.5 Construction of Bayesian Network -- 6.5.1 Structure of Bayesian network and its construction -- 6.5.2 Probabilistic distribution of learning Bayesian network -- 6.5.3 Structure of learning Bayesian network -- 6.6 Bayesian Latent Semantic Model -- 6.7 Semi-supervised Text Mining Algorithms -- 6.7.1 Web page clustering -- 6.7.2 Label documents with latent classification themes -- 6.7.3 Learning labeled and unlabeled data based on naïve Bayesian model -- Exercises -- Chapter 7 Inductive Learning -- 7.1 Introduction -- 7.2 Logic Foundation of Inductive Learning -- 7.2.1 Inductive general paradigm -- 7.2.2 Conditions of concept acquisition -- 7.2.3 Background knowledge of problems -- 7.2.4 Selective and constructive generalization rules -- 7.3 Inductive Bias -- 7.4 Version Space -- 7.4.1 Candidate-elimination algorithm -- 7.4.2 Two improved algorithms -- 7.5 AQ Algorithm for Inductive Learning -- 7.6 Constructing Decision Trees -- 7.7 ID3 Learning Algorithm -- 7.7.1 Introduction to information theory -- 7.7.2 Attribute selection -- 7.7.3 ID3 algorithm -- 7.7.4 Application example of ID3 algorithm -- 7.7.5 Dispersing continuous attribute -- 7.8 Bias Shift Based Decision Tree Algorithm -- 7.8.1 Formalization of bias -- 7.8.2 Bias shift representation -- 7.8.3 Algorithms -- 7.8.4 Procedure bias shift -- 7.8.5 Bias shift based decision tree learning algorithm BSDT -- 7.8.6 Typical case base maintain algorithm TCBM -- 7.8.7 Bias feature extracting algorithm -- 7.8.8 Improved decision tree generating algorithm GSD -- 7.8.9 Experiment results.

7.9 Computational Theories of Inductive Learning -- 7.9.1 Gold's learning theory -- 7.9.2 Model inference -- 7.9.3 Valiant's learning theory -- Exercises -- Chapter 8 Support Vector Machine -- 8.1 Statistical Learning Problem -- 8.1.1 Empirical risk -- 8.1.2 VC Dimension -- 8.2 Consistency of Learning Processes -- 8.2.1 Classical definition of learning consistency -- 8.2.2 Key theorem of learning theory -- 8.2.3 VC entropy -- 8.3 Structural Risk Minimization Inductive Principle -- 8.4 Support Vector Machine -- 8.4.1 Linearly separable case -- 8.4.2 Linearly non-separable case -- 8.5 Kernel Function -- 8.5.1 Polynomial kernel function -- 8.5.2 Radial Basis Function -- 8.5.3 Multi-layer Perceptron -- 8.5.4 Dynamic kernel function -- Exercises -- Chapter 9 Explanation-Based Learning -- 9.1 Introduction -- 9.2 Model for EBL -- 9.3 Explanation-Based Generalization -- 9.3.1 Basic principle -- 9.3.2 Interchange with explanation and generalization -- 9.4 Explanation Generalization using Global Substitutions -- 9.5 Explanation-Based Specialization -- 9.6 Logic Program of Explanation-Based Generalization -- 9.6.1 Operational principle -- 9.6.2 Meta Explanation -- 9.6.3 An example -- 9.7 SOAR Based on Memory Chunks -- 9.8 Operationalization -- 9.8.1 Utility of PRODIGY -- 9.8.2 Operationality of SOAR -- 9.8.3 Operationality of MRS-EBG -- 9.8.4 Operationality of META-LEX -- 9.9 EBL with imperfect domain theory -- 9.9.1 Imperfect domain theory -- 9.9.2 Inverting Resolution -- 9.9.3 Deep knowledge based approach -- Exercises -- Chapter 10 Reinforcement Learning -- 10.1 Introduction -- 10.2 Reinforcement Learning Model -- 10.3 Dynamic Programming -- 10.4 Monte Carlo Methods -- 10.5 Temporal-Difference Learning -- 10.6 Q-Learning -- 10.7 Function Approximation -- 10.8 Reinforcement Learning Applications -- Exercises -- Chapter 11 Rough Set -- 11.1 Introduction.

11.1.1 Categorized View of Knowledge -- 11.1.2 A New Type of Membership Relations -- 11.1.3 The View of Concept's Boundary -- 11.2 Reduction of Knowledge -- 11.2.1 General Reduction -- 11.2.2 Relative Reduction -- 11.2.3 Dependency of Knowledge -- 11.3 Decision Logic -- 11.3.1 Formal Definition of Decision Table -- 11.3.2 Decision Logic Language -- 11.3.3 Semantics of Decision Logic Language -- 11.3.4 Deduction of Decision Logic -- 11.3.5 Standard Expression -- 11.3.6 Decision Rules and Algorithms -- 11.3.7 Inconsistent and Indiscernibility of Decision Rule -- 11.4 Reduction of Decision Tables -- 11.4.1 Dependency of Attributes -- 11.4.2 Reduction of Consistent Decision Tables -- 11.4.3 Reduction of Inconsistent Decision Tables -- 11.5 Extended Model of Rough Sets -- 11.5.1 Variable Precision Rough Set Model -- 11.5.2 Similarity Based Model -- 11.5.3 Rough Set Based Nonmonotonic Logic -- 11.5.4 Integration with Other Mathematical Tools -- 11.6 Experimental Systems of Rough Sets -- 11.7 Granular Computing -- 11.8 Future Trends of Rough Set Theory -- Exercises -- Chapter 12 Association Rules -- 12.1 Introduction -- 12.2 The Apriori Algorithm -- 12.3 FP-Growth Algorithm -- 12.4 CFP-Tree Algorithm -- 12.5 Mining General Fuzzy Association Rules -- 12.6 Distributed Mining Algorithm For Association Rules -- 12.6.1 Generation of candidate sets -- 12.6.2 Local pruning of candidate sets -- 12.6.3 Global pruning of candidate sets -- 12.6.4 Count polling -- 12.6.5 Distributed mining algorithm of association rules -- 12.7 Parallel Mining of Association Rules -- 12.7.1 Count Distribution Algorithm -- 12.7.2 Fast Parallel Mining Algorithm -- 12.7.3 DIC-based algorithm -- 12.7.4 Data skewness and workload balance -- Exercises -- Chapter 13 Evolutionary Computation -- 13.1 Introduction -- 13.2 Formal Model of Evolution System Theory.

13.3 Darwin's Evolutionary Algorithm.