Advanced artificial intelligence
"The joint breakthrough of big data, cloud computing and deep learning has made artificial intelligence (AI) the new focus in the international arena. AI is a branch of computer science, developing intelligent machine with imitating, extending and augmenting human intelligence through artificia...
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| Format: | Buch |
| Sprache: | English |
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New Jersey
World Scientific
[2020]
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| Ausgabe: | Second edition |
| Schriftenreihe: | Series on intelligence science
vol. 4 |
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| 245 | 1 | 0 | |a Advanced artificial intelligence |c Zhongzhi SHI, Chinese Academy of Sciences, China |
| 250 | |a Second edition | ||
| 264 | 1 | |a New Jersey |b World Scientific |c [2020] | |
| 300 | |a xxi, 571 Seiten |b Illustrationen | ||
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| 490 | 1 | |a Series on intelligence science |v vol. 4 | |
| 520 | 3 | |a "The joint breakthrough of big data, cloud computing and deep learning has made artificial intelligence (AI) the new focus in the international arena. AI is a branch of computer science, developing intelligent machine with imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behaviour. This comprehensive compendium, consisting of 15 chapters, captures the updated achievements of AI. It is completely revised to reflect the current researches in the field, through numerous techniques and strategies to address the impending challenges facing computer scientists today. The unique volume is useful for senior or graduate students in the information field and related tertiary specialities. It is also a suitable reference text for professionals, researchers, and academics in AI, machine learning, electrical & electronic engineering and biocomputing"-- | |
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Datensatz im Suchindex
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| adam_text | Contents Preface v About the Author ix Acknowledgments xi Chapter 1 Introduction 1 1.1 1.2 1.3 1.4 1.5 Brief History of AI..................................................................................... 1 Cognitive Issues of AI.............................................................................. 5 Hierarchical Model of Thought............................................................... 7 Symbolic Intelligence.............................................................................. 8 Research Approaches of Artificial Intelligence......................................... 11 1.5.1 Cognitive School............................................................................11 1.5.2 Logical School...............................................................................12 1.5.3 Behavioral School............................................................................12 1.6 Automated Reasoning.................................................................................. 13 1.7 Machine Learning........................................................................................ 16 1.8 Distributed Artificial Intelligence............................................................... 18 1.9 Artificial Thought Model...............................................................................21 1.10 Knowledge-Based Systems ........................................................................ 23 Exercises ................................................................................................................. 26 Chapter 2 Logic
Foundation 2.1 2.2 2.3 29 Introduction ........................................ 29 Logic Programming.....................................................................................32 2.2.1 Definitions of Logic Programming............................................... 32 2.2.2 Data Structure and Recursion in Prolog ......................................34 2.2.3 SLD Resolution...............................................................................34 2.2.4 Non-Logic Components: CUT..................................................... 37 Non-Monotonic Logic..................................................................................41 xiii
xiv Contents 2.4 2.5 2.6 2.7 2.8 Closed World Assumption............................................................................44 Default Logic................................................................................................. 46 Circumscription Logic . . .........................................................................51 Non-Monotonic Logic NML.........................................................................55 Autoepistemic Logic..................................................................................... 57 2.8.1 Moore System Jžffi .........................................................................57 2.8.2 OJz? Logic........................................................................................ 58 2.8.3 Theorems on Normal Forms.........................................................59 2.8.4 -Mark and a Kind of Course of Judging for Stable Expansion.....................................................................................61 2.9 Truth Maintenance System............................................................................64 2.10 Situation Calculus........................................................................................ 70 2.10.1 Many-Sorted Logic for Situation Calculus...................................70 2.10.2 Basic Action Theory in LR............................................................ 71 2.11 Frame Problem.............................................................................................. 72 2.11.1 Frame
Axiom.................................................................................. 73 2.11.2 Criteria for a Solutionto the Frame Problem.................................76 2.11.3 Non-Monotonic Solving Approach of the Frame Problem . . . 78 2.12 Dynamic Description Logic.........................................................................84 2.12.1 Description Logic............................................................................84 2.12.2 Syntax of Dynamic Description Logic......................................... 87 2.12.3 Semantics of Dynamic Description Logic................................... 89 Exercises ..............................................................................................................92 Chapter 3 Constraint Reasoning 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 95 Introduction ..................................................................................................95 Backtracking................................................................................................ 102 Constraint Propagation.................................................................................104 Constraint Propagation in TreeSearch.........................................................106 Intelligent Backtracking and Truth Maintenance .....................................107 Variable Instantiation Orderingand Assignment Ordering...................... 109 Local Revision Search.................................................................................109 Graph-Based
Вackjumping...........................................................................110 Influence-Based Backjumping.................................................................... Ill Constraint Relation Processing.................................................................... 116 3.10.1 Unit Sharing Strategy for Identical Relation...............................116 3.10.2 Interval Propagation....................................................................... 119 3.10.3 Inequality Graph.......................................................................... 120
Contents XV 3.10.4 Inequality Reasoning..................................................................... 121 3.11 Constraint Reasoning System COPS.......................................................... 122 3.12 ILOG Solver................................................................................................ 126 Exercises ................................................................................................................133 Chapter 4 Bayesian Network 135 4.1 Introduction ................................................................................................ 135 4.1.1 History of Bayesian Theory.......................................................... 136 4.1.2 Basic Concepts of the Bayesian Method.....................................136 4.1.3 Applications of Bayesian Network in Data Mining ..................137 4.2 Foundation of Bayesian Probability...........................................................140 4.2.1 Foundation of Probability Theory .............................................. 140 4.2.2 Bayesian Probability....................................................................144 4.3 Bayesian Problem Solving.......................................................................... 147 4.3.1 Common Methods for Prior Distribution Selection..................... 149 4.3.2 Computational Learning..............................................................152 4.3.3 Steps in Bayesian Problem Solving.............................................. 154 4.4 Naïve Bayesian Learning
Model.................................................................157 4.4.1 Naïve Bayesian Learning Model................................................. 157 4.4.2 Boosting of Naïve Bayesian Model.............................................. 160 4.4.3 The Computational Complexity ................................................. 162 4.5 Construction of a Bayesian Network.......................................................... 163 4.5.1 Structure of a Bayesian Network and Its Construction.............163 4.5.2 Probabilistic Distribution of Learning the Bayesian Network..........................................................................................164 4.5.3 Structure of Learning the Bayesian Network.............................. 167 4.6 Bayesian Latent Semantic Model..............................................................171 4.7 Semi-Supervised Text Mining Algorithms................................................. 176 4.7.1 Web Page Clustering.................................................................... 176 4.7.2 Label Documents with Latent Classification Themes.............177 4.7.3 Learning Labeled and Unlabeled Data Based on Naïve Bayesian Model.................................................................178 Exercises ................................................................................................................181 Chapter 5 Probabilistic Graphic Models 5.1 5.2 5.3 183 Introduction ................................................................................................ 183 Graphic
Theory............................................................................................. 185 Hidden Markov Model................................................................................ 186
xvi Contents 5.4 5.5 Conditional Random Field.......................................................................... 190 Inference.......................................................................................................192 5.5.1 Variable Elimination.................................................................... 193 5.5.2 Clique Tree....................................................................................194 5.6 Approximate Inference.................................................................................198 5.6.1 Markov Chain Monte Carlo Methods........................................... 198 5.6.2 Variational Inference....................................................................200 5.7 Probabilistic Graphical Model Learning.................................................... 201 5.7.1 Estimating the Parameters of the Bayesian Network..................201 5.7.2 Estimating the Parameters of Markov Network ........................202 5.8 Topic Model................................................................................................203 Exercises ..............................................................................................................205 Chapter 6 Case-Based Reasoning 6.1 6.2 6.3 6.4 207 Overview...................................................................................................... 207 Basic Notations.............................................................................................209 Process
Model.............................................................................................210 Case Representation....................................................................................214 6.4.1 Semantic Memory Unit.................................................................215 6.4.2 Memory Network.......................................................................... 216 6.5 Case Indexing .............................................................................................218 6.6 Case Retrieval............................................................................................. 219 6.7 Similarity Relations in CBR....................................................................... 222 6.7.1 Semantic Similarity....................................................................... 222 6.7.2 Structural Similarity ....................................................................223 6.7.3 Goal’s Features............................................................................. 224 6.7.4 Individual Similarity....................................................................224 6.7.5 Similarity Assessment .................................................................225 6.8 Case Reuse................................................................................................... 227 6.9 Case Retention............................................................................................. 229 6.10 Instance-Based Learning............................................................................. 230 6.10.1 Learning
Tasks of IBL.................................................................230 6.10.2 Algorithm IBI................................................................................ 232 6.10.3 Reducing Storage Requirements................................................. 232 6.11 Forecast System for Central Fishing Ground ...........................................235 6.11.1 Problem Analysis and Case Representation ...............................236 6.11.2 Similarity Measurement ..............................................................237 6.11.3 Indexing and Retrieval.................................................................239
Contents xvii 6.11.4 Revision with Frame................................................................... 240 6.11.5 Experiments................................................................................... 242 Exercises ................................................................................................................244 Chapter 7 Inductive Learning 7.1 7.2 247 Introduction ................................................................................................247 Logic Foundation of Inductive Learning....................................................249 7.2.1 Inductive General Paradigm ....................................................... 249 7.2.2 Conditions of Concept Acquisition..............................................250 7.2.3 Background Knowledge of Problems.......................................... 252 7.2.4 Selective and Constructive Generalization Rules........................255 7.3 Inductive Bias .............................................................................................259 7.4 Version Space .............................................................................................260 7.4.1 Candidate-Elimination Algorithm ..............................................261 7.4.2 Two Improved Algorithms.......................................................... 264 7.5 AQ Algorithm for Inductive Learning............................ 267 7.6 Constructing Decision Trees.......................................................................268 7.7 ID3 Learning
Algorithm.............................................................................269 7.7.1 Introduction to Information Theory..............................................269 7.7.2 Attribute Selection.......................................................................270 7.7.3 ID3 Algorithm .............................................................................271 7.7.4 Application Example of ID3 Algorithm.................................... 272 7.7.5 Dispersing Continuous Attribute.................................................274 7.8 Bias Shift-Based Decision Tree Algorithm ..............................................275 7.8.1 Formalization of Bias....................................................................276 7.8.2 Bias Shift Representation............................................................. 278 7.8.3 Algorithms ................................................................................... 279 7.8.4 Procedure Bias Shift....................................................................280 7.8.5 Bias Shift-Based Decision Tree Learning Algorithm..................284 7.8.6 Typical Case Base Maintain Algorithm....................................... 284 7.8.7 Bias Feature Extracting Algorithm..............................................285 7.8.8 Improved Decision Tree Generating Algorithm GSD.............286 7.8.9 Experiment Results.......................................................................288 7.9 Computational Theories of Inductive Learning........................................ 290 7.9.1 Gold’s Learning
Theory................................................................ 291 7.9.2 Model Inference.............................................................................292 7.9.3 Valiant’s Learning Theory .......................................................... 294 Exercises ............................................................................................................... 296
xviii Contents Chapter 8 Statistical Learning 299 8.1 8.2 Introduction ................................................................................................ 299 Statistical Learning Problem....................................................................... 301 8.2.1 Empirical Risk............................................................................. 301 8.2.2 VC Dimension............................................................................. 301 8.3 Consistency of Learning Processes .......................................................... 302 8.3.1 Classical Definition of Learning Consistency ........................... 302 8.3.2 Key Theorem of Learning Theory..............................................303 8.3.3 VC Entropy................................................................................... 303 8.4 Structural Risk Minimization Inductive Principle.................................... 305 8.5 Support Vector Machine............................................................................. 308 8.5.1 Linearly Separable Case..............................................................308 8.5.2 Linearly Non-Separable Case....................................................... 311 8.6 Kernel Function ..........................................................................................313 8.6.1 Polynomial Kernel Function....................................................... 313 8.6.2 Radial Basis Function....................................................................314 8.6.3 Multi-Layer
Perceptron.................................................................314 8.6.4 Dynamic Kernel Function..............................................................314 Exercises ................................................................................................................316 Chapter 9 Deep Learning 319 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 Introduction ................................................................................................ 319 Human Brain Visual Mechanism.................................................................321 Autoencoder.................................................. 323 Restricted Boltzmann Machine .................................................................325 Deep Belief Networks................................................................................ 328 Convolutional Neural Networks.................................................................330 Recurrent Neural Networks....................................................................... 337 Long Short-Term Memory.......................................................................... 338 Neural Machine Translation....................................................................... 343 9.9.1 Introduction................................................................................... 343 9.9.2 Model Architecture....................................................................... 344 9.9.3 Quantized Inference....................................................................... 346 Exercises
................................................................................................................348 Chapter 10 Reinforcement Learning 351 10.1 Introduction ................................................................................................ 351 10.2 Reinforcement Learning Model.................................................................354
Contents xix 10.3 Dynamic Programming................................................................................ 357 10.4 Monte Carlo Methods................................................................................ 359 10.5 Temporal-Difference Learning....................................................................361 10.6 2֊Leaming...................................................................................................366 10.7 Function Approximation............................................................................. 369 10.8 Reinforcement Learning Applications....................................................... 371 Exercises ............................................................................................................... 373 Chapter 11 Unsupervised Learning 375 11.1 Introduction ................................................................................................375 11.2 Similarity Measure...................................................................................... 376 11.2.1 Similarity Coefficient....................................................................376 11.2.2 Similarity Measure of Attributes.................................................379 11.3 Partitioning Clustering................................................................................ 380 11.3.1 К-means Algorithm.......................................................................380 11.3.2 K-medoids Algorithm ................................................................ 381 11.3.3 Large Database Partitioning
Method..........................................382 11.4 Hierarchical Clustering Method................................................................ 384 11.4.1 BIRCH Algorithm .......................................................................384 11.4.2 CURE Algorithm..........................................................................385 11.4.3 ROCK Algorithm..........................................................................386 11.5 Density-Based Clustering..........................................................................388 11.6 Grid-Based Clustering................................................................................ 392 11.7 Model-Based Clustering.............................................................................394 11.8 Semi-Supervised Clustering.......................................................................396 11.9 Evaluation of Clustering Methods............................................................ 398 Exercises ............................................................................................................... 400 Chapter 12 Association Rules 12.1 12.2 12.3 12.4 12.5 12.6 401 Introduction ................................................................................................401 The Apriori Algorithm................................................................................ 404 FP-Growth Algorithm................................................................................ 408 CFP-Tree Algorithm...................................................................................
411 Mining General Fuzzy Association Rules.................................................414 Distributed Mining Algorithm for Association Rules..............................417 12.6.1 Generation of Candidate Sets.......................................................418 12.6.2 Local Pruning of Candidate Sets.................................................420 12.6.3 Global Pruning of Candidate Sets ............................................. 421
xx Contents 12.6.4 Count Polling................................................................................ 422 12.6.5 Distributed Mining Algorithm of Association Rules..................423 12.7 Parallel Mining of Association Rules...................................................... 425 12.7.1 Count Distribution Algorithm....................................................... 426 12.7.2 Fast Parallel Mining Algorithm.................................................... 427 12.7.3 DIC-Based Algorithm....................................................................428 12.7.4 Data Skewness and Workload Balance........................................430 Exercises ............................................................................................................... 432 Chapter 13 Evolutionary Computation 435 13.1 13.2 13.3 13.4 13.5 13.6 Introduction ................................................................................................435 Formal Model of Evolution System Theory..............................................437 Darwin’s Evolutionary Algorithm............................................................. 441 Classifier System..........................................................................................442 Bucket Brigade Algorithm.......................................................................... 447 Genetic Algorithm...................................................................................... 449 13.6.1 Major Steps of Genetic Algorithm..............................................450 13.6.2 Representation
Schema.................................................................451 13.6.3 Crossover Operation ....................................................................453 13.6.4 Mutation Operation.......................................................................456 13.6.5 Inversion Operation.......................................................................456 13.7 Parallel Genetic Algorithm.......................................................................... 457 13.8 Classifier System Boole............................................................................. 458 13.9 Rule Discovery System................................................................................ 461 13.10 Evolutionary Strategy ................................................................................ 464 13.11 Evolutionary Programming .......................................................................466 Exercises ................................................................................................................466 Chapter 14 Multi-Agent Systems 469 14.1 Introduction ................................................................................................469 14.2 The Essence of Agent................................................................................ 472 14.2.1 The Concept of Agent....................................................................472 14.2.2 Rational Agent............................................................................. 474 14.2.3 BDI
Model................................................................................... 475 14.3 Agent Architecture...................................................................................... 475 14.3.1 Agent’s Basic Architecture.......................................................... 475 14.3.2 Deliberative Agent.......................................................................477 14.3.3 Reactive Agent............................................................................. 479 14.3.4 Hybrid Agent................................................................................ 481
Contents xxi 14.4 Agent Communication Language............................................................. 483 14.4.1 Agent Communication Introduction..........................................484 14.4.2 FIPA ACL Message.......................................................................486 14.5 Coordination and Cooperation................................................................... 492 14.5.1 Introduction................................................................................... 492 14.5.2 Contract Net Protocol....................................................................495 14.5.3 Partial Global Planning................................................................ 498 14.5.4 Planning Based on Constraint Propagation................................. 501 14.5.5 Ecological-Based Cooperation....................................................505 14.5.6 Game Theory-Based Negotiation.................................................507 14.5.7 Intention-Based Negotiation....................................................... 508 14.5.8 Team-Oriented Collaboration....................................................... 508 14.6 Mobile Agent................................................................................................510 14.7 Multi-Agent Environment MAGE............................................................. 513 14.7.1 The Architecture of MAGE.......................................................... 513 14.7.2 Agent Unified Modeling Language..............................................513 14.7.3 Visual Agent Development
Tool.................................................514 14.7.4 MAGE Running Platform............................................................. 516 14.8 Agent Grid Intelligence Platform ............................................................. 517 Exercises ............................................................................................................... 518 Chapter 15 Internet Intelligence 519 15.1 Introduction ................................................................................................519 15.2 Semantic Web.............................................................................................522 15.3 Ontology...................................................................................................... 527 15.4 Knowledge Graph...................................................................................... 531 15.5 Cloud Computing ...................................................................................... 533 15.6 Edge Computing..........................................................................................536 15.7 Collective Intelligence................................................................................ 539 15.8 Crowd Intelligence...................................................................................... 540 Exercises ............................................................................................................... 545 Bibliography 547 Author Index 563 Subject Index 565
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| id | DE-604.BV046106210 |
| illustrated | Illustrated |
| indexdate | 2024-12-24T07:46:50Z |
| institution | BVB |
| isbn | 9789811200878 9811200874 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031486921 |
| oclc_num | 1122732972 |
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| owner | DE-739 DE-573 DE-355 DE-BY-UBR |
| owner_facet | DE-739 DE-573 DE-355 DE-BY-UBR |
| physical | xxi, 571 Seiten Illustrationen |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | World Scientific |
| record_format | marc |
| series | Series on intelligence science |
| series2 | Series on intelligence science |
| spellingShingle | Shi, Zhongzhi Advanced artificial intelligence Series on intelligence science 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, Chinese Academy of Sciences, China |
| title_fullStr | Advanced artificial intelligence Zhongzhi SHI, Chinese Academy of Sciences, China |
| title_full_unstemmed | Advanced artificial intelligence Zhongzhi SHI, Chinese Academy of Sciences, China |
| title_short | Advanced artificial intelligence |
| title_sort | advanced artificial intelligence |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031486921&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV040664920 |
| work_keys_str_mv | AT shizhongzhi advancedartificialintelligence |