Advanced artificial intelligence /:
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 behavio...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore ; Hackensack, NJ :
World Scientific,
©2011.
|
| Schriftenreihe: | Series on intelligence science ;
v. 1. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 resource (xvi, 613 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 585-613). |
| ISBN: | 9789814291354 9814291358 |
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| 100 | 1 | |a Shi, Zhongzhi. | |
| 245 | 1 | 0 | |a Advanced artificial intelligence / |c Zhongzhi Shi. |
| 260 | |a Singapore ; |a Hackensack, NJ : |b World Scientific, |c ©2011. | ||
| 300 | |a 1 online resource (xvi, 613 pages) : |b illustrations | ||
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| 490 | 1 | |a Series on intelligence science ; |v v. 1 | |
| 504 | |a Includes bibliographical references (pages 585-613). | ||
| 505 | 0 | 0 | |g Machine generated contents note: |g ch. 1 |t Introduction -- |g 1.1. |t Brief History of AI -- |g 1.2. |t Cognitive Issues of AI -- |g 1.3. |t Hierarchical Model of Thought -- |g 1.4. |t Symbolic Intelligence -- |g 1.5. |t Research Approaches of Artificial Intelligence -- |g 1.6. |t Automated Reasoning -- |g 1.7. |t Machine Learning -- |g 1.8. |t Distributed Artificial Intelligence -- |g 1.9. |t Artificial Thought Model -- |g 1.10. |t Knowledge Based Systems -- |t Exercises -- |g ch. 2 |t Logic Foundation of Artificial Intelligence -- |g 2.1. |t Introduction -- |g 2.2. |t Logic Programming -- |g 2.3. |t Nonmonotonic Logic -- |g 2.4. |t Closed World Assumption -- |g 2.5. |t Default Logic -- |g 2.6. |t Circumscription Logic -- |g 2.7. |t Nonmonotonic Logic NML -- |g 2.8. |t Autoepistemic Logic -- |g 2.9. |t Truth Maintenance System -- |g 2.10. |t Situation Calculus -- |g 2.11. |t Frame Problem -- |g 2.12. |t Dynamic Description Logic -- |t Exercises -- |g ch. 3 |t Constraint Reasoning -- |g 3.1. |t Introduction -- |g 3.2. |t Backtracking -- |g 3.3. |t Constraint Propagation -- |g 3.4. |t Constraint Propagation in Tree Search -- |g 3.5. |t Intelligent Backtracking and Truth Maintenance. |
| 505 | 0 | 0 | |g 3.6. |t Variable Instantiation Ordering and Assignment Ordering -- |g 3.7. |t Local Revision Search -- |g 3.8. |t Graph-based Backjumping -- |g 3.9. |t Influence-based Backjumping -- |g 3.10. |t Constraint Relation Processing -- |g 3.11. |t Constraint Reasoning System COPS -- |g 3.12. |t ILOG Solver -- |t Exercise -- |g ch. 4 |t Qualitative Reasoning -- |g 4.1. |t Introduction -- |g 4.2. |t Basic approaches in qualitative reasoning -- |g 4.3. |t Qualitative Model -- |g 4.4. |t Qualitative Process -- |g 4.5. |t Qualitative Simulation Reasoning -- |g 4.6. |t Algebra Approach -- |g 4.7. |t Spatial Geometric Qualitative Reasoning -- |t Exercises -- |g ch. 5 |t Case-Based Reasoning -- |g 5.1. |t Overview -- |g 5.2. |t Basic Notations -- |g 5.3. |t Process Model -- |g 5.4. |t Case Representation -- |g 5.5. |t Case Indexing -- |g 5.6. |t Case Retrieval -- |g 5.7. |t Similarity Relations in CBR -- |g 5.8. |t Case Reuse -- |g 5.9. |t Case Retainion -- |g 5.10. |t Instance-Based Learning -- |g 5.11. |t Forecast System for Central Fishing Ground -- |t Exercises -- |g ch. 6 |t Probabilistic Reasoning -- |g 6.1. |t Introduction -- |g 6.2. |t Foundation of Bayesian Probability -- |g 6.3. |t Bayesian Problem Solving -- |g 6.4. |t Naive Bayesian Learning Model. |
| 505 | 0 | 0 | |g 6.5. |t Construction of Bayesian Network -- |g 6.6. |t Bayesian Latent Semantic Model -- |g 6.7. |t Semi-supervised Text Mining Algorithms -- |t Exercises -- |g ch. 7 |t Inductive Learning -- |g 7.1. |t Introduction -- |g 7.2. |t Logic Foundation of Inductive Learning -- |g 7.3. |t Inductive Bias -- |g 7.4. |t Version Space -- |g 7.5. |t AQ Algorithm for Inductive Learning -- |g 7.6. |t Constructing Decision Trees -- |g 7.7. |t ID3 Learning Algorithm -- |g 7.8. |t Bias Shift Based Decision Tree Algorithm -- |g 7.9. |t Computational Theories of Inductive Learning -- |t Exercises -- |g ch. 8 |t Support Vector Machine -- |g 8.1. |t Statistical Learning Problem -- |g 8.2. |t Consistency of Learning Processes -- |g 8.3. |t Structural Risk Minimization Inductive Principle -- |g 8.4. |t Support Vector Machine -- |g 8.5. |t Kernel Function -- |t Exercises -- |g ch. 9 |t Explanation-Based Learning -- |g 9.1. |t Introduction -- |g 9.2. |t Model for EBL -- |g 9.3. |t Explanation-Based Generalization -- |g 9.4. |t Explanation Generalization using Global Substitutions -- |g 9.5. |t Explanation-Based Specialization -- |g 9.6. |t Logic Program of Explanation-Based Generalization -- |g 9.7. |t SOAR Based on Memory Chunks. |
| 505 | 0 | 0 | |g 9.8. |t Operationalization -- |g 9.9. |t EBL with imperfect domain theory -- |t Exercises -- |g ch. 10 |t Reinforcement Learning -- |g 10.1. |t Introduction -- |g 10.2. |t Reinforcement Learning Model -- |g 10.3. |t Dynamic Programming -- |g 10.4. |t Monte Carlo Methods -- |g 10.5. |t Temporal-Difference Learning -- |g 10.6. |t Q-Learning -- |g 10.7. |t Function Approximation -- |g 10.8. |t Reinforcement Learning Applications -- |t Exercises -- |g ch. 11 |t Rough Set -- |g 11.1. |t Introduction -- |g 11.2. |t Reduction of Knowledge -- |g 11.3. |t Decision Logic -- |g 11.4. |t Reduction of Decision Tables -- |g 11.5. |t Extended Model of Rough Sets -- |g 11.6. |t Experimental Systems of Rough Sets -- |g 11.7. |t Granular Computing -- |g 11.8. |t Future Trends of Rough Set Theory -- |t Exercises -- |g ch. 12 |t Association Rules -- |g 12.1. |t Introduction -- |g 12.2. |t The Apriori Algorithm -- |g 12.3. |t FP-Growth Algorithm -- |g 12.4. |t CFP-Tree Algorithm -- |g 12.5. |t Mining General Fuzzy Association Rules -- |g 12.6. |t Distributed Mining Algorithm For Association Rules -- |g 12.7. |t Parallel Mining of Association Rules -- |t Exercises -- |g ch. 13 |t Evolutionary Computation -- |g 13.1. |t Introduction -- |g 13.2. |t Formal Model of Evolution System Theory. |
| 505 | 0 | 0 | |g 13.3. |t Darwin's Evolutionary Algorithm -- |g 13.4. |t Classifier System -- |g 13.5. |t Bucket Brigade Algorithm -- |g 13.6. |t Genetic Algorithm -- |g 13.7. |t Parallel Genetic Algorithm -- |g 13.8. |t Classifier System Boole -- |g 13.9. |t Rule Discovery System -- |g 13.10. |t Evolutionary Strategy -- |g 13.11. |t Evolutionary Programming -- |t Exercises -- |g ch. 14 |t Distributed Intelligence -- |g 14.1. |t Introduction -- |g 14.2. |t The Essence of Agent -- |g 14.3. |t Agent Architecture -- |g 14.4. |t Agent Communication Language ACL -- |g 14.5. |t Coordination and Cooperation -- |g 14.6. |t Mobile Agent -- |g 14.7. |t Multi-Agent Environment MAGE -- |g 14.8. |t Agent Grid Intelligence Platform -- |t Exercises -- |g ch. 15 |t Artificial Life -- |g 15.1. |t Introduction -- |g 15.2. |t Exploration of Artificial Life -- |g 15.3. |t Artificial Life Model -- |g 15.4. |t Research Approach of Artificial Life -- |g 15.5. |t Cellular Automata -- |g 15.6. |t Morphogenesis Theory -- |g 15.7. |t Chaos Theories -- |g 15.8. |t Experimental Systems of Artificial Life -- |t Exercises. |
| 588 | 0 | |a Print version record. | |
| 520 | |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. | ||
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
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| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Intelligence artificielle. |2 ram | |
| 776 | 0 | 8 | |i Print version: |a Shi, Zhongzhi. |t Advanced artificial intelligence. |d Singapore ; Hackensack, NJ : World Scientific, ©2011 |z 9789814291347 |w (OCoLC)456173446 |
| 830 | 0 | |a Series on intelligence science ; |v v. 1. |0 http://id.loc.gov/authorities/names/no2011110846 | |
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| contents | Introduction -- Brief History of AI -- Cognitive Issues of AI -- Hierarchical Model of Thought -- Symbolic Intelligence -- Research Approaches of Artificial Intelligence -- Automated Reasoning -- Machine Learning -- Distributed Artificial Intelligence -- Artificial Thought Model -- Knowledge Based Systems -- Exercises -- Logic Foundation of Artificial Intelligence -- Logic Programming -- Nonmonotonic Logic -- Closed World Assumption -- Default Logic -- Circumscription Logic -- Nonmonotonic Logic NML -- Autoepistemic Logic -- Truth Maintenance System -- Situation Calculus -- Frame Problem -- Dynamic Description Logic -- Constraint Reasoning -- Backtracking -- Constraint Propagation -- Constraint Propagation in Tree Search -- Intelligent Backtracking and Truth Maintenance. Variable Instantiation Ordering and Assignment Ordering -- Local Revision Search -- Graph-based Backjumping -- Influence-based Backjumping -- Constraint Relation Processing -- Constraint Reasoning System COPS -- ILOG Solver -- Exercise -- Qualitative Reasoning -- Basic approaches in qualitative reasoning -- Qualitative Model -- Qualitative Process -- Qualitative Simulation Reasoning -- Algebra Approach -- Spatial Geometric Qualitative Reasoning -- Case-Based Reasoning -- Overview -- Basic Notations -- Process Model -- Case Representation -- Case Indexing -- Case Retrieval -- Similarity Relations in CBR -- Case Reuse -- Case Retainion -- Instance-Based Learning -- Forecast System for Central Fishing Ground -- Probabilistic Reasoning -- Foundation of Bayesian Probability -- Bayesian Problem Solving -- Naive Bayesian Learning Model. Construction of Bayesian Network -- Bayesian Latent Semantic Model -- Semi-supervised Text Mining Algorithms -- Inductive Learning -- Logic Foundation of Inductive Learning -- Inductive Bias -- Version Space -- AQ Algorithm for Inductive Learning -- Constructing Decision Trees -- ID3 Learning Algorithm -- Bias Shift Based Decision Tree Algorithm -- Computational Theories of Inductive Learning -- Support Vector Machine -- Statistical Learning Problem -- Consistency of Learning Processes -- Structural Risk Minimization Inductive Principle -- Kernel Function -- Explanation-Based Learning -- Model for EBL -- Explanation-Based Generalization -- Explanation Generalization using Global Substitutions -- Explanation-Based Specialization -- Logic Program of Explanation-Based Generalization -- SOAR Based on Memory Chunks. Operationalization -- EBL with imperfect domain theory -- Reinforcement Learning -- Reinforcement Learning Model -- Dynamic Programming -- Monte Carlo Methods -- Temporal-Difference Learning -- Q-Learning -- Function Approximation -- Reinforcement Learning Applications -- Rough Set -- Reduction of Knowledge -- Decision Logic -- Reduction of Decision Tables -- Extended Model of Rough Sets -- Experimental Systems of Rough Sets -- Granular Computing -- Future Trends of Rough Set Theory -- Association Rules -- The Apriori Algorithm -- FP-Growth Algorithm -- CFP-Tree Algorithm -- Mining General Fuzzy Association Rules -- Distributed Mining Algorithm For Association Rules -- Parallel Mining of Association Rules -- Evolutionary Computation -- Formal Model of Evolution System Theory. Darwin's Evolutionary Algorithm -- Classifier System -- Bucket Brigade Algorithm -- Genetic Algorithm -- Parallel Genetic Algorithm -- Classifier System Boole -- Rule Discovery System -- Evolutionary Strategy -- Evolutionary Programming -- Distributed Intelligence -- The Essence of Agent -- Agent Architecture -- Agent Communication Language ACL -- Coordination and Cooperation -- Mobile Agent -- Multi-Agent Environment MAGE -- Agent Grid Intelligence Platform -- Artificial Life -- Exploration of Artificial Life -- Artificial Life Model -- Research Approach of Artificial Life -- Cellular Automata -- Morphogenesis Theory -- Chaos Theories -- Experimental Systems of Artificial Life -- Exercises. |
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| discipline | Informatik |
| format | Electronic eBook |
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code="g">4.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">4.2.</subfield><subfield code="t">Basic approaches in qualitative reasoning --</subfield><subfield code="g">4.3.</subfield><subfield code="t">Qualitative Model --</subfield><subfield code="g">4.4.</subfield><subfield code="t">Qualitative Process --</subfield><subfield code="g">4.5.</subfield><subfield code="t">Qualitative Simulation Reasoning --</subfield><subfield code="g">4.6.</subfield><subfield code="t">Algebra Approach --</subfield><subfield code="g">4.7.</subfield><subfield code="t">Spatial Geometric Qualitative Reasoning --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 5</subfield><subfield code="t">Case-Based Reasoning --</subfield><subfield code="g">5.1.</subfield><subfield code="t">Overview --</subfield><subfield code="g">5.2.</subfield><subfield code="t">Basic Notations --</subfield><subfield code="g">5.3.</subfield><subfield code="t">Process Model --</subfield><subfield code="g">5.4.</subfield><subfield code="t">Case Representation --</subfield><subfield code="g">5.5.</subfield><subfield code="t">Case Indexing --</subfield><subfield code="g">5.6.</subfield><subfield code="t">Case Retrieval --</subfield><subfield code="g">5.7.</subfield><subfield code="t">Similarity Relations in CBR --</subfield><subfield code="g">5.8.</subfield><subfield code="t">Case Reuse --</subfield><subfield code="g">5.9.</subfield><subfield code="t">Case Retainion --</subfield><subfield code="g">5.10.</subfield><subfield code="t">Instance-Based Learning --</subfield><subfield code="g">5.11.</subfield><subfield code="t">Forecast System for Central Fishing Ground --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 6</subfield><subfield code="t">Probabilistic Reasoning --</subfield><subfield code="g">6.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">6.2.</subfield><subfield code="t">Foundation of Bayesian Probability --</subfield><subfield code="g">6.3.</subfield><subfield code="t">Bayesian Problem Solving --</subfield><subfield code="g">6.4.</subfield><subfield code="t">Naive Bayesian Learning Model.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="g">6.5.</subfield><subfield code="t">Construction of Bayesian Network --</subfield><subfield code="g">6.6.</subfield><subfield code="t">Bayesian Latent Semantic Model --</subfield><subfield code="g">6.7.</subfield><subfield code="t">Semi-supervised Text Mining Algorithms --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 7</subfield><subfield code="t">Inductive Learning --</subfield><subfield code="g">7.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">7.2.</subfield><subfield code="t">Logic Foundation of Inductive Learning --</subfield><subfield code="g">7.3.</subfield><subfield code="t">Inductive Bias --</subfield><subfield code="g">7.4.</subfield><subfield code="t">Version Space --</subfield><subfield code="g">7.5.</subfield><subfield code="t">AQ Algorithm for Inductive Learning --</subfield><subfield code="g">7.6.</subfield><subfield code="t">Constructing Decision Trees --</subfield><subfield code="g">7.7.</subfield><subfield code="t">ID3 Learning Algorithm --</subfield><subfield code="g">7.8.</subfield><subfield code="t">Bias Shift Based Decision Tree Algorithm --</subfield><subfield code="g">7.9.</subfield><subfield code="t">Computational Theories of Inductive Learning --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 8</subfield><subfield code="t">Support Vector Machine --</subfield><subfield code="g">8.1.</subfield><subfield code="t">Statistical Learning Problem --</subfield><subfield code="g">8.2.</subfield><subfield code="t">Consistency of Learning Processes --</subfield><subfield code="g">8.3.</subfield><subfield code="t">Structural Risk Minimization Inductive Principle --</subfield><subfield code="g">8.4.</subfield><subfield code="t">Support Vector Machine --</subfield><subfield code="g">8.5.</subfield><subfield code="t">Kernel Function --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 9</subfield><subfield code="t">Explanation-Based Learning --</subfield><subfield code="g">9.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">9.2.</subfield><subfield code="t">Model for EBL --</subfield><subfield code="g">9.3.</subfield><subfield code="t">Explanation-Based Generalization --</subfield><subfield code="g">9.4.</subfield><subfield code="t">Explanation Generalization using Global Substitutions --</subfield><subfield code="g">9.5.</subfield><subfield code="t">Explanation-Based Specialization --</subfield><subfield code="g">9.6.</subfield><subfield code="t">Logic Program of Explanation-Based Generalization --</subfield><subfield code="g">9.7.</subfield><subfield code="t">SOAR Based on Memory Chunks.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="g">9.8.</subfield><subfield code="t">Operationalization --</subfield><subfield code="g">9.9.</subfield><subfield code="t">EBL with imperfect domain theory --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 10</subfield><subfield code="t">Reinforcement Learning --</subfield><subfield code="g">10.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">10.2.</subfield><subfield code="t">Reinforcement Learning Model --</subfield><subfield code="g">10.3.</subfield><subfield code="t">Dynamic Programming --</subfield><subfield code="g">10.4.</subfield><subfield code="t">Monte Carlo Methods --</subfield><subfield code="g">10.5.</subfield><subfield code="t">Temporal-Difference Learning --</subfield><subfield code="g">10.6.</subfield><subfield code="t">Q-Learning --</subfield><subfield code="g">10.7.</subfield><subfield code="t">Function Approximation --</subfield><subfield code="g">10.8.</subfield><subfield code="t">Reinforcement Learning Applications --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 11</subfield><subfield code="t">Rough Set --</subfield><subfield code="g">11.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">11.2.</subfield><subfield code="t">Reduction of Knowledge --</subfield><subfield code="g">11.3.</subfield><subfield code="t">Decision Logic --</subfield><subfield code="g">11.4.</subfield><subfield code="t">Reduction of Decision Tables --</subfield><subfield code="g">11.5.</subfield><subfield code="t">Extended Model of Rough Sets --</subfield><subfield code="g">11.6.</subfield><subfield code="t">Experimental Systems of Rough Sets --</subfield><subfield code="g">11.7.</subfield><subfield code="t">Granular Computing --</subfield><subfield code="g">11.8.</subfield><subfield code="t">Future Trends of Rough Set Theory --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 12</subfield><subfield code="t">Association Rules --</subfield><subfield code="g">12.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">12.2.</subfield><subfield code="t">The Apriori Algorithm --</subfield><subfield code="g">12.3.</subfield><subfield code="t">FP-Growth Algorithm --</subfield><subfield code="g">12.4.</subfield><subfield code="t">CFP-Tree Algorithm --</subfield><subfield code="g">12.5.</subfield><subfield code="t">Mining General Fuzzy Association Rules --</subfield><subfield code="g">12.6.</subfield><subfield code="t">Distributed Mining Algorithm For Association Rules --</subfield><subfield code="g">12.7.</subfield><subfield code="t">Parallel Mining of Association Rules --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 13</subfield><subfield code="t">Evolutionary Computation --</subfield><subfield code="g">13.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">13.2.</subfield><subfield code="t">Formal Model of Evolution System Theory.</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="g">13.3.</subfield><subfield code="t">Darwin's Evolutionary Algorithm --</subfield><subfield code="g">13.4.</subfield><subfield code="t">Classifier System --</subfield><subfield code="g">13.5.</subfield><subfield code="t">Bucket Brigade Algorithm --</subfield><subfield code="g">13.6.</subfield><subfield code="t">Genetic Algorithm --</subfield><subfield code="g">13.7.</subfield><subfield code="t">Parallel Genetic Algorithm --</subfield><subfield code="g">13.8.</subfield><subfield code="t">Classifier System Boole --</subfield><subfield code="g">13.9.</subfield><subfield code="t">Rule Discovery System --</subfield><subfield code="g">13.10.</subfield><subfield code="t">Evolutionary Strategy --</subfield><subfield code="g">13.11.</subfield><subfield code="t">Evolutionary Programming --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 14</subfield><subfield code="t">Distributed Intelligence --</subfield><subfield code="g">14.1.</subfield><subfield code="t">Introduction --</subfield><subfield code="g">14.2.</subfield><subfield code="t">The Essence of Agent --</subfield><subfield code="g">14.3.</subfield><subfield code="t">Agent Architecture --</subfield><subfield code="g">14.4.</subfield><subfield code="t">Agent Communication Language ACL --</subfield><subfield code="g">14.5.</subfield><subfield code="t">Coordination and Cooperation --</subfield><subfield code="g">14.6.</subfield><subfield code="t">Mobile Agent --</subfield><subfield code="g">14.7.</subfield><subfield code="t">Multi-Agent Environment MAGE --</subfield><subfield code="g">14.8.</subfield><subfield code="t">Agent Grid Intelligence Platform --</subfield><subfield code="t">Exercises --</subfield><subfield code="g">ch. 15</subfield><subfield code="t">Artificial Life --</subfield><subfield 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| id | ZDB-4-EBU-ocn754765355 |
| illustrated | Illustrated |
| indexdate | 2024-11-26T14:49:03Z |
| institution | BVB |
| isbn | 9789814291354 9814291358 |
| language | English |
| oclc_num | 754765355 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xvi, 613 pages) : illustrations |
| psigel | ZDB-4-EBU |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series on intelligence science ; |
| series2 | Series on intelligence science ; |
| spelling | Shi, Zhongzhi. Advanced artificial intelligence / Zhongzhi Shi. Singapore ; Hackensack, NJ : World Scientific, ©2011. 1 online resource (xvi, 613 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Series on intelligence science ; v. 1 Includes bibliographical references (pages 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. Print version record. 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. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Intelligence artificielle. ram Print version: Shi, Zhongzhi. Advanced artificial intelligence. Singapore ; Hackensack, NJ : World Scientific, ©2011 9789814291347 (OCoLC)456173446 Series on intelligence science ; v. 1. http://id.loc.gov/authorities/names/no2011110846 FWS01 ZDB-4-EBU FWS_PDA_EBU https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=389611 Volltext |
| spellingShingle | Shi, Zhongzhi Advanced artificial intelligence / Series on intelligence science ; Introduction -- Brief History of AI -- Cognitive Issues of AI -- Hierarchical Model of Thought -- Symbolic Intelligence -- Research Approaches of Artificial Intelligence -- Automated Reasoning -- Machine Learning -- Distributed Artificial Intelligence -- Artificial Thought Model -- Knowledge Based Systems -- Exercises -- Logic Foundation of Artificial Intelligence -- Logic Programming -- Nonmonotonic Logic -- Closed World Assumption -- Default Logic -- Circumscription Logic -- Nonmonotonic Logic NML -- Autoepistemic Logic -- Truth Maintenance System -- Situation Calculus -- Frame Problem -- Dynamic Description Logic -- Constraint Reasoning -- Backtracking -- Constraint Propagation -- Constraint Propagation in Tree Search -- Intelligent Backtracking and Truth Maintenance. Variable Instantiation Ordering and Assignment Ordering -- Local Revision Search -- Graph-based Backjumping -- Influence-based Backjumping -- Constraint Relation Processing -- Constraint Reasoning System COPS -- ILOG Solver -- Exercise -- Qualitative Reasoning -- Basic approaches in qualitative reasoning -- Qualitative Model -- Qualitative Process -- Qualitative Simulation Reasoning -- Algebra Approach -- Spatial Geometric Qualitative Reasoning -- Case-Based Reasoning -- Overview -- Basic Notations -- Process Model -- Case Representation -- Case Indexing -- Case Retrieval -- Similarity Relations in CBR -- Case Reuse -- Case Retainion -- Instance-Based Learning -- Forecast System for Central Fishing Ground -- Probabilistic Reasoning -- Foundation of Bayesian Probability -- Bayesian Problem Solving -- Naive Bayesian Learning Model. Construction of Bayesian Network -- Bayesian Latent Semantic Model -- Semi-supervised Text Mining Algorithms -- Inductive Learning -- Logic Foundation of Inductive Learning -- Inductive Bias -- Version Space -- AQ Algorithm for Inductive Learning -- Constructing Decision Trees -- ID3 Learning Algorithm -- Bias Shift Based Decision Tree Algorithm -- Computational Theories of Inductive Learning -- Support Vector Machine -- Statistical Learning Problem -- Consistency of Learning Processes -- Structural Risk Minimization Inductive Principle -- Kernel Function -- Explanation-Based Learning -- Model for EBL -- Explanation-Based Generalization -- Explanation Generalization using Global Substitutions -- Explanation-Based Specialization -- Logic Program of Explanation-Based Generalization -- SOAR Based on Memory Chunks. Operationalization -- EBL with imperfect domain theory -- Reinforcement Learning -- Reinforcement Learning Model -- Dynamic Programming -- Monte Carlo Methods -- Temporal-Difference Learning -- Q-Learning -- Function Approximation -- Reinforcement Learning Applications -- Rough Set -- Reduction of Knowledge -- Decision Logic -- Reduction of Decision Tables -- Extended Model of Rough Sets -- Experimental Systems of Rough Sets -- Granular Computing -- Future Trends of Rough Set Theory -- Association Rules -- The Apriori Algorithm -- FP-Growth Algorithm -- CFP-Tree Algorithm -- Mining General Fuzzy Association Rules -- Distributed Mining Algorithm For Association Rules -- Parallel Mining of Association Rules -- Evolutionary Computation -- Formal Model of Evolution System Theory. Darwin's Evolutionary Algorithm -- Classifier System -- Bucket Brigade Algorithm -- Genetic Algorithm -- Parallel Genetic Algorithm -- Classifier System Boole -- Rule Discovery System -- Evolutionary Strategy -- Evolutionary Programming -- Distributed Intelligence -- The Essence of Agent -- Agent Architecture -- Agent Communication Language ACL -- Coordination and Cooperation -- Mobile Agent -- Multi-Agent Environment MAGE -- Agent Grid Intelligence Platform -- Artificial Life -- Exploration of Artificial Life -- Artificial Life Model -- Research Approach of Artificial Life -- Cellular Automata -- Morphogenesis Theory -- Chaos Theories -- Experimental Systems of Artificial Life -- Exercises. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Intelligence artificielle. ram |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D001185 |
| title | Advanced artificial intelligence / |
| title_alt | Introduction -- Brief History of AI -- Cognitive Issues of AI -- Hierarchical Model of Thought -- Symbolic Intelligence -- Research Approaches of Artificial Intelligence -- Automated Reasoning -- Machine Learning -- Distributed Artificial Intelligence -- Artificial Thought Model -- Knowledge Based Systems -- Exercises -- Logic Foundation of Artificial Intelligence -- Logic Programming -- Nonmonotonic Logic -- Closed World Assumption -- Default Logic -- Circumscription Logic -- Nonmonotonic Logic NML -- Autoepistemic Logic -- Truth Maintenance System -- Situation Calculus -- Frame Problem -- Dynamic Description Logic -- Constraint Reasoning -- Backtracking -- Constraint Propagation -- Constraint Propagation in Tree Search -- Intelligent Backtracking and Truth Maintenance. Variable Instantiation Ordering and Assignment Ordering -- Local Revision Search -- Graph-based Backjumping -- Influence-based Backjumping -- Constraint Relation Processing -- Constraint Reasoning System COPS -- ILOG Solver -- Exercise -- Qualitative Reasoning -- Basic approaches in qualitative reasoning -- Qualitative Model -- Qualitative Process -- Qualitative Simulation Reasoning -- Algebra Approach -- Spatial Geometric Qualitative Reasoning -- Case-Based Reasoning -- Overview -- Basic Notations -- Process Model -- Case Representation -- Case Indexing -- Case Retrieval -- Similarity Relations in CBR -- Case Reuse -- Case Retainion -- Instance-Based Learning -- Forecast System for Central Fishing Ground -- Probabilistic Reasoning -- Foundation of Bayesian Probability -- Bayesian Problem Solving -- Naive Bayesian Learning Model. Construction of Bayesian Network -- Bayesian Latent Semantic Model -- Semi-supervised Text Mining Algorithms -- Inductive Learning -- Logic Foundation of Inductive Learning -- Inductive Bias -- Version Space -- AQ Algorithm for Inductive Learning -- Constructing Decision Trees -- ID3 Learning Algorithm -- Bias Shift Based Decision Tree Algorithm -- Computational Theories of Inductive Learning -- Support Vector Machine -- Statistical Learning Problem -- Consistency of Learning Processes -- Structural Risk Minimization Inductive Principle -- Kernel Function -- Explanation-Based Learning -- Model for EBL -- Explanation-Based Generalization -- Explanation Generalization using Global Substitutions -- Explanation-Based Specialization -- Logic Program of Explanation-Based Generalization -- SOAR Based on Memory Chunks. Operationalization -- EBL with imperfect domain theory -- Reinforcement Learning -- Reinforcement Learning Model -- Dynamic Programming -- Monte Carlo Methods -- Temporal-Difference Learning -- Q-Learning -- Function Approximation -- Reinforcement Learning Applications -- Rough Set -- Reduction of Knowledge -- Decision Logic -- Reduction of Decision Tables -- Extended Model of Rough Sets -- Experimental Systems of Rough Sets -- Granular Computing -- Future Trends of Rough Set Theory -- Association Rules -- The Apriori Algorithm -- FP-Growth Algorithm -- CFP-Tree Algorithm -- Mining General Fuzzy Association Rules -- Distributed Mining Algorithm For Association Rules -- Parallel Mining of Association Rules -- Evolutionary Computation -- Formal Model of Evolution System Theory. Darwin's Evolutionary Algorithm -- Classifier System -- Bucket Brigade Algorithm -- Genetic Algorithm -- Parallel Genetic Algorithm -- Classifier System Boole -- Rule Discovery System -- Evolutionary Strategy -- Evolutionary Programming -- Distributed Intelligence -- The Essence of Agent -- Agent Architecture -- Agent Communication Language ACL -- Coordination and Cooperation -- Mobile Agent -- Multi-Agent Environment MAGE -- Agent Grid Intelligence Platform -- Artificial Life -- Exploration of Artificial Life -- Artificial Life Model -- Research Approach of Artificial Life -- Cellular Automata -- Morphogenesis Theory -- Chaos Theories -- Experimental Systems of Artificial Life -- Exercises. |
| 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 | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Intelligence artificielle. artificial intelligence. aat COMPUTERS Enterprise Applications Business Intelligence Tools. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Intelligence artificielle. ram |
| topic_facet | Artificial intelligence. Artificial Intelligence Intelligence artificielle. artificial intelligence. COMPUTERS Enterprise Applications Business Intelligence Tools. COMPUTERS Intelligence (AI) & Semantics. Artificial intelligence |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=389611 |
| work_keys_str_mv | AT shizhongzhi advancedartificialintelligence |