Advanced artificial intelligence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey
World Scientific
[2011]
|
| Schriftenreihe: | Series on intelligence science
vol. 1 |
| Schlagworte: | |
| Online-Zugang: | UER01 Volltext |
| Beschreibung: | Includes bibliographical references (p. 585-613) 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 Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior. Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel. It reflects the research updates in this field and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel |
| Beschreibung: | 1 Online-Ressource (xvi, 613 p.) |
| ISBN: | 9789814291347 981429134X 9789814291354 9814291358 |
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| 245 | 1 | 0 | |a Advanced artificial intelligence |c Zhongzhi Shi |
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| 300 | |a 1 Online-Ressource (xvi, 613 p.) | ||
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| 490 | 0 | |a Series on intelligence science |v vol. 1 | |
| 500 | |a Includes bibliographical references (p. 585-613) | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a 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 | ||
| 500 | |a Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior. Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel. It reflects the research updates in this field and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel | ||
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Datensatz im Suchindex
| _version_ | 1804175296645038080 |
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| any_adam_object | |
| author | Shi, Zhongzhi |
| author_GND | (DE-588)1014634504 |
| author_facet | Shi, Zhongzhi |
| author_role | aut |
| author_sort | Shi, Zhongzhi |
| author_variant | z s zs |
| building | Verbundindex |
| bvnumber | BV042968450 |
| collection | ZDB-4-EBA ZDB-4-EBU |
| ctrlnum | (OCoLC)754765355 (DE-599)BVBBV042968450 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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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</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior. Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel. It reflects the research updates in this field and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. 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| id | DE-604.BV042968450 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:14:02Z |
| institution | BVB |
| isbn | 9789814291347 981429134X 9789814291354 9814291358 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028394317 |
| oclc_num | 754765355 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 DE-29 |
| owner_facet | DE-1046 DE-1047 DE-29 |
| physical | 1 Online-Ressource (xvi, 613 p.) |
| psigel | ZDB-4-EBA ZDB-4-EBU FAW_PDA_EBA FLA_PDA_EBU ZDB-4-EBA UER_PDA_EBA_Kauf |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | World Scientific |
| record_format | marc |
| series2 | Series on intelligence science |
| spelling | Shi, Zhongzhi Verfasser (DE-588)1014634504 aut Advanced artificial intelligence Zhongzhi Shi New Jersey World Scientific [2011] 1 Online-Ressource (xvi, 613 p.) txt rdacontent c rdamedia cr rdacarrier Series on intelligence science vol. 1 Includes bibliographical references (p. 585-613) 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 Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior. Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel. It reflects the research updates in this field and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s 1\p DE-604 http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=389611 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Shi, Zhongzhi Advanced artificial intelligence COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 |
| title | Advanced artificial intelligence |
| title_auth | Advanced artificial intelligence |
| title_exact_search | Advanced artificial intelligence |
| title_full | Advanced artificial intelligence Zhongzhi Shi |
| title_fullStr | Advanced artificial intelligence Zhongzhi Shi |
| title_full_unstemmed | Advanced artificial intelligence Zhongzhi Shi |
| title_short | Advanced artificial intelligence |
| title_sort | advanced artificial intelligence |
| topic | COMPUTERS / Enterprise Applications / Business Intelligence Tools bisacsh COMPUTERS / Intelligence (AI) & Semantics bisacsh Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | COMPUTERS / Enterprise Applications / Business Intelligence Tools COMPUTERS / Intelligence (AI) & Semantics Künstliche Intelligenz Artificial intelligence |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=389611 |
| work_keys_str_mv | AT shizhongzhi advancedartificialintelligence |