소장자료
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100 | 1 | ▼aShi, Zhongzhi.▲ | |
245 | 1 | 0 | ▼aAdvanced artificial intelligence /▼cZhongzhi Shi.▲ |
260 | ▼aSingapore ;▼aHackensack, NJ :▼bWorld Scientific,▼cc2011.▲ | ||
300 | ▼axvi, 613 p. :▼bill. ;▼c26 cm.▲ | ||
490 | 1 | ▼aSeries on intelligence science ;▼vv. 1▲ | |
504 | ▼aIncludes bibliographical references (p. 585-613).▲ | ||
505 | 0 | 0 | ▼gMachine generated contents note:▼gch. 1▼tIntroduction --▼g1.1.▼tBrief History of AI --▼g1.2.▼tCognitive Issues of AI --▼g1.3.▼tHierarchical Model of Thought --▼g1.4.▼tSymbolic Intelligence --▼g1.5.▼tResearch Approaches of Artificial Intelligence --▼g1.6.▼tAutomated Reasoning --▼g1.7.▼tMachine Learning --▼g1.8.▼tDistributed Artificial Intelligence --▼g1.9.▼tArtificial Thought Model --▼g1.10.▼tKnowledge Based Systems --▼tExercises --▼gch. 2▼tLogic Foundation of Artificial Intelligence --▼g2.1.▼tIntroduction --▼g2.2.▼tLogic Programming --▼g2.3.▼tNonmonotonic Logic --▼g2.4.▼tClosed World Assumption --▼g2.5.▼tDefault Logic --▼g2.6.▼tCircumscription Logic --▼g2.7.▼tNonmonotonic Logic NML --▼g2.8.▼tAutoepistemic Logic --▼g2.9.▼tTruth Maintenance System --▼g2.10.▼tSituation Calculus --▼g2.11.▼tFrame Problem --▼g2.12.▼tDynamic Description Logic --▼tExercises --▼gch. 3▼tConstraint Reasoning --▼g3.1.▼tIntroduction --▼g3.2.▼tBacktracking --▼g3.3.▼tConstraint Propagation --▼g3.4.▼tConstraint Propagation in Tree Search --▼g3.5.▼tIntelligent Backtracking and Truth Maintenance▲ |
505 | 0 | 0 | ▼g3.6.▼tVariable Instantiation Ordering and Assignment Ordering --▼g3.7.▼tLocal Revision Search --▼g3.8.▼tGraph-based Backjumping --▼g3.9.▼tInfluence-based Backjumping --▼g3.10.▼tConstraint Relation Processing --▼g3.11.▼tConstraint Reasoning System COPS --▼g3.12.▼tILOG Solver --▼tExercise --▼gch. 4▼tQualitative Reasoning --▼g4.1.▼tIntroduction --▼g4.2.▼tBasic approaches in qualitative reasoning --▼g4.3.▼tQualitative Model --▼g4.4.▼tQualitative Process --▼g4.5.▼tQualitative Simulation Reasoning --▼g4.6.▼tAlgebra Approach --▼g4.7.▼tSpatial Geometric Qualitative Reasoning --▼tExercises --▼gch. 5▼tCase-Based Reasoning --▼g5.1.▼tOverview --▼g5.2.▼tBasic Notations --▼g5.3.▼tProcess Model --▼g5.4.▼tCase Representation --▼g5.5.▼tCase Indexing --▼g5.6.▼tCase Retrieval --▼g5.7.▼tSimilarity Relations in CBR --▼g5.8.▼tCase Reuse --▼g5.9.▼tCase Retainion --▼g5.10.▼tInstance-Based Learning --▼g5.11.▼tForecast System for Central Fishing Ground --▼tExercises --▼gch. 6▼tProbabilistic Reasoning --▼g6.1.▼tIntroduction --▼g6.2.▼tFoundation of Bayesian Probability --▼g6.3.▼tBayesian Problem Solving --▼g6.4.▼tNaive Bayesian Learning Model▲ |
505 | 0 | 0 | ▼g6.5.▼tConstruction of Bayesian Network --▼g6.6.▼tBayesian Latent Semantic Model --▼g6.7.▼tSemi-supervised Text Mining Algorithms --▼tExercises --▼gch. 7▼tInductive Learning --▼g7.1.▼tIntroduction --▼g7.2.▼tLogic Foundation of Inductive Learning --▼g7.3.▼tInductive Bias --▼g7.4.▼tVersion Space --▼g7.5.▼tAQ Algorithm for Inductive Learning --▼g7.6.▼tConstructing Decision Trees --▼g7.7.▼tID3 Learning Algorithm --▼g7.8.▼tBias Shift Based Decision Tree Algorithm --▼g7.9.▼tComputational Theories of Inductive Learning --▼tExercises --▼gch. 8▼tSupport Vector Machine --▼g8.1.▼tStatistical Learning Problem --▼g8.2.▼tConsistency of Learning Processes --▼g8.3.▼tStructural Risk Minimization Inductive Principle --▼g8.4.▼tSupport Vector Machine --▼g8.5.▼tKernel Function --▼tExercises --▼gch. 9▼tExplanation-Based Learning --▼g9.1.▼tIntroduction --▼g9.2.▼tModel for EBL --▼g9.3.▼tExplanation-Based Generalization --▼g9.4.▼tExplanation Generalization using Global Substitutions --▼g9.5.▼tExplanation-Based Specialization --▼g9.6.▼tLogic Program of Explanation-Based Generalization --▼g9.7.▼tSOAR Based on Memory Chunks▲ |
505 | 0 | 0 | ▼g9.8.▼tOperationalization --▼g9.9.▼tEBL with imperfect domain theory --▼tExercises --▼gch. 10▼tReinforcement Learning --▼g10.1.▼tIntroduction --▼g10.2.▼tReinforcement Learning Model --▼g10.3.▼tDynamic Programming --▼g10.4.▼tMonte Carlo Methods --▼g10.5.▼tTemporal-Difference Learning --▼g10.6.▼tQ-Learning --▼g10.7.▼tFunction Approximation --▼g10.8.▼tReinforcement Learning Applications --▼tExercises --▼gch. 11▼tRough Set --▼g11.1.▼tIntroduction --▼g11.2.▼tReduction of Knowledge --▼g11.3.▼tDecision Logic --▼g11.4.▼tReduction of Decision Tables --▼g11.5.▼tExtended Model of Rough Sets --▼g11.6.▼tExperimental Systems of Rough Sets --▼g11.7.▼tGranular Computing --▼g11.8.▼tFuture Trends of Rough Set Theory --▼tExercises --▼gch. 12▼tAssociation Rules --▼g12.1.▼tIntroduction --▼g12.2.▼tThe Apriori Algorithm --▼g12.3.▼tFP-Growth Algorithm --▼g12.4.▼tCFP-Tree Algorithm --▼g12.5.▼tMining General Fuzzy Association Rules --▼g12.6.▼tDistributed Mining Algorithm For Association Rules --▼g12.7.▼tParallel Mining of Association Rules --▼tExercises --▼gch. 13▼tEvolutionary Computation --▼g13.1.▼tIntroduction --▼g13.2.▼tFormal Model of Evolution System Theory▲ |
505 | 0 | 0 | ▼g13.3.▼tDarwin's Evolutionary Algorithm --▼g13.4.▼tClassifier System --▼g13.5.▼tBucket Brigade Algorithm --▼g13.6.▼tGenetic Algorithm --▼g13.7.▼tParallel Genetic Algorithm --▼g13.8.▼tClassifier System Boole --▼g13.9.▼tRule Discovery System --▼g13.10.▼tEvolutionary Strategy --▼g13.11.▼tEvolutionary Programming --▼tExercises --▼gch. 14▼tDistributed Intelligence --▼g14.1.▼tIntroduction --▼g14.2.▼tThe Essence of Agent --▼g14.3.▼tAgent Architecture --▼g14.4.▼tAgent Communication Language ACL --▼g14.5.▼tCoordination and Cooperation --▼g14.6.▼tMobile Agent --▼g14.7.▼tMulti-Agent Environment MAGE --▼g14.8.▼tAgent Grid Intelligence Platform --▼tExercises --▼gch. 15▼tArtificial Life --▼g15.1.▼tIntroduction --▼g15.2.▼tExploration of Artificial Life --▼g15.3.▼tArtificial Life Model --▼g15.4.▼tResearch Approach of Artificial Life --▼g15.5.▼tCellular Automata --▼g15.6.▼tMorphogenesis Theory --▼g15.7.▼tChaos Theories --▼g15.8.▼tExperimental Systems of Artificial Life --▼tExercises.▲ |
650 | 0 | ▼aArtificial intelligence.▲ | |
650 | 7 | ▼aIntelligence artificielle.▼2ram▲ | |
830 | 0 | ▼aSeries on intelligence science ;▼vv. 1.▲ | |
999 | ▼c장화옥▲ |
![](https://lib.pusan.ac.kr/wp-content/themes/pnul2022/assets/images/default/default_w_279X393.png)
Advanced artificial intelligence
자료유형
국외단행본
서명/책임사항
Advanced artificial intelligence / Zhongzhi Shi.
개인저자
발행사항
Singapore ; Hackensack, NJ : World Scientific , c2011.
형태사항
xvi, 613 p. : ill. ; 26 cm.
총서사항
서지주기
Includes bibliographical references (p. 585-613).
내용주기
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.
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.
ISBN
9789814291347 981429134X
청구기호
006.3 S555a
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