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Advanced artificial intelligence

Author: Zhongzhi Shi
Publisher: Hackensack, N.J. : World Scientific, 2011
Series: Series on intelligence science, v. 1
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Artificial Intelligence is a branch of computer science and a discipline in the study of machine intelligence. This book consists of 16 chapters on artificial intelligence. It discusses the methods and technology from theory, algorithm, system and applications related to artificial intelligence. It is intended for senior students or graduates.
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Document Type: Book
All Authors / Contributors: Zhongzhi Shi
ISBN: 9789814291347 981429134X
OCLC Number: 769992953
Notes: Machine generated contents note:. ch. 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. 1.6. Automated Reasoning. 1.7. Machine Learning. 1.8. Distributed Artificial Intelligence. 1.9. Artificial Thought Model. 1.10. Knowledge Based Systems. Exercises. ch. 2. Logic Foundation of Artificial Intelligence. 2.1. Introduction. 2.2. Logic Programming. 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. 2.9. Truth Maintenance System. 2.10. Situation Calculus. 2.11. Frame Problem. 2.12. Dynamic Description Logic. Exercises. ch. 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. 3.11. Constraint Reasoning System COPS. 3.12. ILOG Solver. Exercise. ch. 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. 4.6. Algebra Approach. 4.7. Spatial Geometric Qualitative Reasoning. Exercises. ch. 5. Case-Based Reasoning. 5.1. Overview. 5.2. Basic Notations. 5.3. Process Model. 5.4. Case Representation. 5.5. Case Indexing. 5.6. Case Retrieval. 5.7. Similarity Relations in CBR. 5.8. Case Reuse. 5.9. Case Retainion. 5.10. Instance-Based Learning. 5.11. Forecast System for Central Fishing Ground. Exercises. ch. 6. Probabilistic Reasoning. 6.1. Introduction. 6.2. Foundation of Bayesian Probability. 6.3. Bayesian Problem Solving. 6.4. Naive Bayesian Learning Model
6.5. Construction of Bayesian Network. 6.6. Bayesian Latent Semantic Model. 6.7. Semi-supervised Text Mining Algorithms. Exercises. ch. 7. Inductive Learning. 7.1. Introduction. 7.2. Logic Foundation of Inductive Learning. 7.3. Inductive Bias. 7.4. Version Space. 7.5. AQ Algorithm for Inductive Learning. 7.6. Constructing Decision Trees. 7.7. ID3 Learning Algorithm. 7.8. Bias Shift Based Decision Tree Algorithm. 7.9. Computational Theories of Inductive Learning. Exercises. ch. 8. Support Vector Machine. 8.1. Statistical Learning Problem. 8.2. Consistency of Learning Processes. 8.3. Structural Risk Minimization Inductive Principle. 8.4. Support Vector Machine. 8.5. Kernel Function. Exercises. ch. 9. Explanation-Based Learning. 9.1. Introduction. 9.2. Model for EBL. 9.3. Explanation-Based Generalization. 9.4. Explanation Generalization using Global Substitutions. 9.5. Explanation-Based Specialization. 9.6. Logic Program of Explanation-Based Generalization. 9.7. SOAR Based on Memory Chunks
9.8. Operationalization. 9.9. EBL with imperfect domain theory. Exercises. ch. 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. ch. 11. Rough Set. 11.1. Introduction. 11.2. Reduction of Knowledge. 11.3. Decision Logic. 11.4. Reduction of Decision Tables. 11.5. Extended Model of Rough Sets. 11.6. Experimental Systems of Rough Sets. 11.7. Granular Computing. 11.8. Future Trends of Rough Set Theory. Exercises. ch. 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. 12.7. Parallel Mining of Association Rules. Exercises. ch. 13. Evolutionary Computation. 13.1. Introduction. 13.2. Formal Model of Evolution System Theory
13.3. Darwin's Evolutionary Algorithm. 13.4. Classifier System. 13.5. Bucket Brigade Algorithm. 13.6. Genetic Algorithm. 13.7. Parallel Genetic Algorithm. 13.8. Classifier System Boole. 13.9. Rule Discovery System. 13.10. Evolutionary Strategy. 13.11. Evolutionary Programming. Exercises. ch. 14. Distributed Intelligence. 14.1. Introduction. 14.2. The Essence of Agent. 14.3. Agent Architecture. 14.4. Agent Communication Language ACL. 14.5. Coordination and Cooperation. 14.6. Mobile Agent. 14.7. Multi-Agent Environment MAGE. 14.8. Agent Grid Intelligence Platform. Exercises. ch. 15. Artificial Life. 15.1. Introduction. 15.2. Exploration of Artificial Life. 15.3. Artificial Life Model. 15.4. Research Approach of Artificial Life. 15.5. Cellular Automata. 15.6. Morphogenesis Theory. 15.7. Chaos Theories. 15.8. Experimental Systems of Artificial Life. Exercises
Description: xvi, 613 s. : illustrations ; 26 cm
Contents: Machine generated contents note:. ch --
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 --
1.6. Automated Reasoning --
1.7. Machine Learning --
1.8. Distributed Artificial Intelligence --
1.9. Artificial Thought Model --
1.10. Knowledge Based Systems --
Exercises --
ch --
2. Logic Foundation of Artificial Intelligence --
2.1. Introduction --
2.2. Logic Programming --
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 --
2.9. Truth Maintenance System --
2.10. Situation Calculus --
2.11. Frame Problem --
2.12. Dynamic Description Logic --
Exercises --
ch --
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 --
3.11. Constraint Reasoning System COPS. 3.12. ILOG Solver --
Exercise --
ch --
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 --
4.6. Algebra Approach --
4.7. Spatial Geometric Qualitative Reasoning --
Exercises --
ch --
5. Case-Based Reasoning --
5.1. Overview --
5.2. Basic Notations --
5.3. Process Model --
5.4. Case Representation --
5.5. Case Indexing --
5.6. Case Retrieval --
5.7. Similarity Relations in CBR. 5.8. Case Reuse --
5.9. Case Retainion --
5.10. Instance-Based Learning --
5.11. Forecast System for Central Fishing Ground --
Exercises --
ch --
6. Probabilistic Reasoning --
6.1. Introduction --
6.2. Foundation of Bayesian Probability --
6.3. Bayesian Problem Solving --
6.4. Naive Bayesian Learning Model. 6.5. Construction of Bayesian Network --
6.6. Bayesian Latent Semantic Model --
6.7. Semi-supervised Text Mining Algorithms --
Exercises --
ch --
7. Inductive Learning --
7.1. Introduction --
7.2. Logic Foundation of Inductive Learning --
7.3. Inductive Bias --
7.4. Version Space --
7.5. AQ Algorithm for Inductive Learning --
7.6. Constructing Decision Trees --
7.7. ID3 Learning Algorithm --
7.8. Bias Shift Based Decision Tree Algorithm --
7.9. Computational Theories of Inductive Learning --
Exercises --
ch --
8. Support Vector Machine --
8.1. Statistical Learning Problem --
8.2. Consistency of Learning Processes --
8.3. Structural Risk Minimization Inductive Principle --
8.4. Support Vector Machine --
8.5. Kernel Function --
Exercises --
ch --
9. Explanation-Based Learning --
9.1. Introduction --
9.2. Model for EBL. 9.3. Explanation-Based Generalization --
9.4. Explanation Generalization using Global Substitutions --
9.5. Explanation-Based Specialization --
9.6. Logic Program of Explanation-Based Generalization --
9.7. SOAR Based on Memory Chunks. 9.8. Operationalization --
9.9. EBL with imperfect domain theory --
Exercises --
ch --
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 --
ch --
11. Rough Set --
11.1. Introduction --
11.2. Reduction of Knowledge --
11.3. Decision Logic --
11.4. Reduction of Decision Tables --
11.5. Extended Model of Rough Sets --
11.6. Experimental Systems of Rough Sets --
11.7. Granular Computing --
11.8. Future Trends of Rough Set Theory --
Exercises --
ch --
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 --
12.7. Parallel Mining of Association Rules --
Exercises --
ch --
13. Evolutionary Computation --
13.1. Introduction --
13.2. Formal Model of Evolution System Theory. 13.3. Darwin's Evolutionary Algorithm --
13.4. Classifier System --
13.5. Bucket Brigade Algorithm --
13.6. Genetic Algorithm --
13.7. Parallel Genetic Algorithm --
13.8. Classifier System Boole --
13.9. Rule Discovery System --
13.10. Evolutionary Strategy --
13.11. Evolutionary Programming --
Exercises --
ch --
14. Distributed Intelligence --
14.1. Introduction --
14.2. The Essence of Agent --
14.3. Agent Architecture --
14.4. Agent Communication Language ACL. 14.5. Coordination and Cooperation --
14.6. Mobile Agent --
14.7. Multi-Agent Environment MAGE. 14.8. Agent Grid Intelligence Platform --
Exercises --
ch --
15. Artificial Life --
15.1. Introduction --
15.2. Exploration of Artificial Life --
15.3. Artificial Life Model --
15.4. Research Approach of Artificial Life --
15.5. Cellular Automata --
15.6. Morphogenesis Theory --
15.7. Chaos Theories --
15.8. Experimental Systems of Artificial Life --
Exercises.
Series Title: Series on intelligence science, v. 1
Responsibility: Zhongzhi Shi

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Artificial Intelligence is a branch of computer science and a discipline in the study of machine intelligence. This book consists of 16 chapters on artificial intelligence. It discusses the methods  Read more...

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   schema:description "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 -- 3.11. Constraint Reasoning System COPS. 3.12. ILOG Solver -- Exercise -- ch -- 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 -- 4.6. Algebra Approach -- 4.7. Spatial Geometric Qualitative Reasoning -- Exercises -- ch -- 5. Case-Based Reasoning -- 5.1. Overview -- 5.2. Basic Notations -- 5.3. Process Model -- 5.4. Case Representation -- 5.5. Case Indexing -- 5.6. Case Retrieval -- 5.7. Similarity Relations in CBR. 5.8. Case Reuse -- 5.9. Case Retainion -- 5.10. Instance-Based Learning -- 5.11. Forecast System for Central Fishing Ground -- Exercises -- ch -- 6. Probabilistic Reasoning -- 6.1. Introduction -- 6.2. Foundation of Bayesian Probability -- 6.3. Bayesian Problem Solving -- 6.4. Naive Bayesian Learning Model." ;
   schema:description "9.8. Operationalization -- 9.9. EBL with imperfect domain theory -- Exercises -- ch -- 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 -- ch -- 11. Rough Set -- 11.1. Introduction -- 11.2. Reduction of Knowledge -- 11.3. Decision Logic -- 11.4. Reduction of Decision Tables -- 11.5. Extended Model of Rough Sets -- 11.6. Experimental Systems of Rough Sets -- 11.7. Granular Computing -- 11.8. Future Trends of Rough Set Theory -- Exercises -- ch -- 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 -- 12.7. Parallel Mining of Association Rules -- Exercises -- ch -- 13. Evolutionary Computation -- 13.1. Introduction -- 13.2. Formal Model of Evolution System Theory." ;
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