Verfügbarkeit: Fundamentals of artificial intelligence

Fundamentals of artificial intelligence:

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1. Verfasser:	Chowdhary, K. R. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	New Delhi Springer India [2020]
Schlagworte:	Künstliche Intelligenz
Online-Zugang:	Inhaltsverzeichnis Klappentext
Beschreibung:	xxx, 716 Seiten Illustrationen
ISBN:	9788132239703

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adam_text	Contents 1 Introducing Artificial Intelligence........................................................... 1.1 Introduction..................................................................................... 1.2 The Turing Test.............................................................................. 1.3 Goals of AI..................................................................................... 1.4 Roots of AI..................................................................................... 1.4.1 Philosophy...................................................................... 1.4.2 Logic and Mathematics................................................. 1.4.3 Computation.................................................................... 1.4.4 Psychology and Cognitive Science............................... 1.4.5 Biology and Neuroscience............................................. 1.4.6 Evolution......................................................................... 1.5 Artificial Consciousness.................................................................. 1.6 Techniques Used in AI.................................................................. 1.7 Sub-fields of AI.............................................................................. 1.7.1 Speech Processing........................................................ 1.7.2 Natural Language Processing........................................ 1.7.3 Planning........................................................................... 1.7.4 Engineering and Expert Systems................................. 1.7.5 Fuzzy Systems............................................................... 1.7.6 Models of Brain and Evolution................................... 1.8 Perception, Understanding, and Action........................................ 1.9 Physical Symbol System Hypothesis........................................... 1.9.1 Formal System............................................................... 1.9.2 Symbols and Physical Symbol Systems..................... 1.9.3 Formal Logic.................................................................. 1.9.4 The Stored Program Concept........................................ 1.10 Considerations for KnowledgeRepresentation.............................. 1.10.1 Defining the Knowledge............................................... 1.10.2 Objective of Knowledge Representation..................... 1 1 3 4 5 6 6 7 7 8 8 9 9 10 11 11 12 12 13 13 14 14 15 15 16 16 16 17 17 xiii xiv 2 3 Contents 1.10.3 Requirements of a Knowledge Representation......... 1.10.4 Practical Aspects of Representations ......................... 1.10.5 Components of a Representation................................ 1.11 Knowledge Representation Using Natural Language................ 1.12 Summary...................................................................................... Exercises.................................................................................................... References................................................................................................. 18 18 19 20 20 22 23 Logic 2.1 2.2 2.3 2.4 and Reasoning Patterns............................................................... Introduction................................................................................. Argumentation Theory.................................................................. Role of Knowledge...................................................................... Propositional Logic...................................................................... 2.4.1 Interpretation of Formulas........................................... 2.4.2 Logical Consequence.................................................. 2.4.3 Syntax and Semantics of an Expression.................... 2.4.4 Semantic Tableau......................................................... 2.5 Reasoning Patterns...................................................................... 2.5.1 Rule-Based Reasoning ............................................... 2.5.2 Model-Based Reasoning............................................. 2.6 Proof Methods............................................................................. 2.6.1 Normal Forms............................................................. 2.6.2 Resolution.................................................................... 2.6.3 Properties of Inference Rules...................................... 2.7 Nonmonotonic Reasoning........................................................... 2.8 Hilbert and the Axiomatic Approach........................................ 2.8.1 Roots and Early Stages............................................... 2.8.2 Axiomàtics and Formalism........................................ 2.9 Summary...................................................................................... Exercises................................................................................................... References................................................................................................. 25 25 27 28 28 31 32 33 33 35 38 38 39 40 40 41 42 43 44 45 47 48 49 First Order Predicate Logic.................................................................. 3.1 Introduction................................................................................. 3.2 Representation in Predicate Logic ............................................. 3.3 Syntax and Semantics................................................................. 3.4 Conversion to Clausal Form ...................................................... 3.5 Substitutions and Unification...................................................... 3.5.1 Composition of Substitutions...................................... 3.5.2 Unification................................................................... 3.6 Resolution Principle................................................................... 3.6.1 Theorem Proving Formalism...................................... 3.6.2 Proof by Resolution................................................... 3.7 Complexity of Resolution Proof................................................. 51 51 52 55 57 59 60 61 62 64 64 65 Contents XV 3.8 Interpretation and Inferences........................................................ 3.8.1 Herbrand’s Universe..................................................... 3.8.2 Herbrand’s Theorem..................................................... 3.8.3 The Procedural Interpretation........................................ 3.9 Most General Unifiers................................................................... 3.9.1 Lifting............................................................................. 3.9.2 Unification Algorithm................................................... 3.10 Unfounded Sets ............................................................................. 3.11 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 66 68 71 72 76 78 79 81 83 84 88 4 Rule Based Reasoning............................................................................... 4.1 Introduction.................................................................................... 4.2 An Overview of RBS................................................................... 4.3 Forward Chaining.......................................................................... 4.3.1 Forward Chaining Algorithm........................................ 4.3.2 Conflict Resolution........................................................ 4.3.3 Efficiency in Rule Selection.......................................... 4.3.4 Complexity of Preconditions........................................ 4.4 Backward Chaining........................................................................ 4.4.1 Backward Chaining Algorithm................................... 4.4.2 Goal Determination........................................................ 4.5 Forward Versus Backward Chaining .......................................... 4.6 Typical RB System........................................................................ 4.7 Other Systems of Reasoning........................................................ 4.7.1 Model-Based Systems................................................... 4.7.2 Case-Based Reasoning................................................. 4.8 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 89 89 91 93 93 95 97 98 98 99 100 100 102 102 103 104 105 106 109 5 Logic 5.1 5.2 5.3 5.4 Ill Ill 112 114 116 117 119 120 121 122 124 129 5.5 5.6 5.7 Programming and Prolog............................................................ Introduction.................................................................................... Logic Programming........................................................................ Interpretation of Horn Clauses in Rule- Chaining....................... Logic Versus Control................................................................... 5.4.1 Data Structures............................................................... 5.4.2 Procedure-Call Execution............................................ 5.4.3 Backward Versus Forward Reasoning....................... 5.4.4 Path Finding Algorithm................................................. Expressing Control Information................................................... Running Simple Programs............................................................ Some Built-In Predicates............................................................... xvi 6 7 Contents 5.8 Recursive Programming............................................................... 5.9 List Manipulation........................................................................ 5.10 Arithmetic Expressions............................................................... 5.11 Backtracking, Cuts and Negation............................................... 5.12 Efficiency Considerations for PrologPrograms.......................... 5.13 Summary...................................................................................... Exercises................................................................................................... References................................................................................................. 130 132 135 135 137 138 139 141 Real-World Knowledge Representation and Reasoning..................... 6.1 Introduction................................................................................. 6.2 Taxonomic Reasoning.................................................................. 6.3 Techniques for Commonsense Reasoning.................................. 6.4 Ontologies.................................................................................... 6.5 Ontology Structures...................................................................... 6.5.1 Language and Reasoning............................................. 6.5.2 Levels of Ontologies.................................................... 6.5.3 WordNet...................................................................... 6.5.4 Axioms and First-Order Logic.................................... 6.5.5 Sowa’s Ontology......................................................... 6.6 Reasoning Using Ontologies...................................................... 6.6.1 Categories and Objects............................................... 6.6.2 Physical Decomposition of Categories....................... 6.6.3 Measurements............................................................... 6.6.4 Object-Oriented Analysis............................................. 6.7 Ontological Engineering............................................................. 6.8 Situation Calculus........................................................................ 6.8.1 Action, Situation, and Objects.................................... 6.8.2 Formalism.................................................................... 6.8.3 Formalizing the Notions of Context........................... 6.9 Nonmonotonic Reasoning.......................................................... 6.10 Default Reasoning........................................................................ 6.10.1 Notion of a Default...................................................... 6.10.2 The Syntax of Default Logic...................................... 6.10.3 Algorithm for Default Reasoning............................... 6.11 Summary..................................................................................... Exercises................................................................................................... References................................................................................................ 143 143 144 147 148 150 151 152 153 154 154 156 156 157 157 157 158 159 159 160 163 165 166 168 169 170 172 172 176 Networks-Based Representation........................................................... 7.1 Introduction................................................................................. 7.2 Semantic Networks..................................................................... 7.2.1 Syntax and Semantics of Semantics Networks......... 7.2.2 Human Knowledge Creation...................................... 179 179 180 182 184 Contents 8 xvii 7.2.3 Semantic Nets and Natural LanguageProcessing ... 7.2.4 Performance.................................................................... 7.3 Conceptual Graphs........................................................................ 7.4 Frames and Reasoning.................................................................... 7.4.1 Inheritance Hierarchies................................................. 7.4.2 Slots Terminology ........................................................ 7.4.3 Frame Languages.......................................................... 7.4.4 Case Study...................................................................... 7.5 Description Logic........................................................................... 7.5.1 Definitions and Sentence Structures............................ 7.5.2 Concept Language........................................................ 7.5.3 Architecture for Ջ if Knowledge Representation............................................................... 7.5.4 Value Restrictions.......................................................... 7.5.5 Reasoning and Inferences............................................ 7.6 Conceptual Dependencies............................................................. 7.6.1 The Parser...................................................................... 7.6.2 Conceptual Dependency and Inferences..................... 7.6.3 Scripts............................................................................. 7.6.4 Conceptual Dependency Versus Semantic Nets......... 7.7 Summary......................................................................................... Exercises....................................................................................................... References.................................................................................................... 184 185 185 188 189 190 191 192 195 196 197 State Space Search.................................................................................... 8.1 Introduction.................................................................................... 8.2 Representation of Search............................................................... 8.3 Graph Search Basics...................................................................... 8.4 Complexities of State-Space Search............................................ 8.5 Uninformed Search........................................................................ 8.5.1 Breadth-First Search...................................................... 8.5.2 Depth-First Search........................................................ 8.5.3 Analysis of BFS and DFS............................................ 8.5.4 Depth-First Iterative Deepening Search..................... 8.5.5 Bidirectional Search...................................................... 8.6 Memory Requirements for Search Algorithms............................ 8.6.1 Depth-First Searches...................................................... 8.6.2 Breadth-First Searches................................................... 8.7 Problem Formulation for Search................................................... 8.8 Summary......................................................................................... Exercises....................................................................................................... References..................................................................................................... 217 217 218 219 220 222 222 224 225 227 228 229 229 230 230 232 232 236 201 202 203 204 207 209 210 211 212 213 215 xviii Contents 9 Heuristic Search...................................................................................... 9.1 Introduction................................................................................. 9.2 Heuristic Approach...................................................................... 9.3 Hill-Climbing Methods............................................................... 9.4 Best-First Search.......................................................................... 9.4.1 GBFS Algorithm........................................................ 9.4.2 Analysis of Best-First Search...................................... 9.5 Heuristic Determination of Minimum Cost Paths .................... 9.5.1 Search Algorithm A.................................................... 9.5.2 The Evaluation Function............................................. 9.5.3 Analysis of A Search.................................................. 9.5.4 Optimality of Algorithm A*......................................... 9.6 Comparison of Heuristics Approaches...................................... 9.7 Simulated Annealing.................................................................... 9.8 Genetic Algorithms...................................................................... 9.8.1 Exploring Different Structures.................................... 9.8.2 Process of Innovation in Human............................... 9.8.3 Mutation Operator ...................................................... 9.8.4 GA Applications........................................................... 9.9 Summary...................................................................................... Exercises................................................................................................... References................................................................................................. 239 239 241 242 244 245 247 249 249 251 253 254 254 256 259 260 261 261 261 263 265 271 10 Constraint Satisfaction Problems......................................................... 10.1 Introduction................................................................................. 10.2 CSP Applications........................................................................ 10.3 Representation of CSP................................................................. 10.3.1 Constraints in CSP...................................................... 10.3.2 Variables in CSP........................................................ 10.4 Solving a CSP............................................................................ 10.4.1 Synthesizing the Constraints...................................... 10.4.2 An Extended Theory for Synthesizing...................... 10.5 Solution Approaches to CSPs ................................................... 10.6 CSP Algorithms.......................................................................... 10.6.1 Generate and Test........................................................ 10.6.2 Backtracking............................................................... 10.6.3 Efficiency Considerations .......................................... 10.7 Propagating of Constraints.......................................................... 10.7.1 Forward Checking...................................................... 10.7.2 Degree of Heuristics................................................... 10.8 Cryptarithmetics.......................................................................... 10.9 Theoretical Aspects of CSPs..................................................... 273 273 274 276 277 279 280 281 283 285 287 288 288 292 293 294 294 295 298 Contents XIX 10.10 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 299 299 302 11 Adversarial Search and Game Theory................................................. 11.1 Introduction.................................................................................... 11.2 Classification of Games................................................................. 11.3 Game Playing Strategy................................................................. 11.4 Two-Person Zero-Sum Games..................................................... 11.5 The Prisoner’s Dilemma............................................................... 11.6 Two-Player Game Strategies........................................................ 11.7 Games of Perfect Information...................................................... 11.8 Games of Imperfect Information ................................................. 11.9 Nash Arbitration Scheme............................................................... 11.10 n-Person Games............................................................................. 11.11 Representation of Two-Player Games.......................................... 11.12 Minimax Search............................................................................. 11.13 Tic-tac-toe Game Analysis............................................................ 11.14 Alpha-Beta Search ........................................................................ 11.14.1 Complexities Analysis of Alpha-Beta.......................... 11.14.2 Improving the Efficiency of Alpha-Beta..................... 11.15 Sponsored Search.......................................................................... 11.16 Playing Chess with Computer...................................................... 11.17 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 303 303 305 306 307 308 310 312 312 314 316 317 318 321 324 326 327 328 329 329 330 335 12 Reasoning in Uncertain Environments................................................. 12.1 Introduction.................................................................................... 12.2 Foundations of Probability Theory............................................... 12.3 Conditional Probability and Bayes Theorem.............................. 12.4 Bayesian Networks........................................................................ 12.4.1 Constructing a Bayesian Network.............................. 12.4.2 Bayesian Network for Chain of Variables ................ 12.4.3 Independence of Variables .......................................... 12.4.4 Propagation in Bayesian Belief Networks................... 12.4.5 Causality and Independence........................................ 12.4.6 Hidden Markov Models............................................... 12.4.7 Construction Process of Bayesian Networks.............. 12.5 Dempster-Shafer Theory of Evidence.......................................... 12.5.1 Dempster-Shafer Rule of Combination ..................... 12.5.2 Dempster-Shafer Versus Bayes Theory..................... 12.6 Fuzzy Sets, Fuzzy Logic, and Fuzzy Inferences....................... 337 337 339 340 344 344 345 347 348 351 353 354 356 357 358 361 XX 13 14 Contents 12.6.1 Fuzzy Composition Relation........................................ 12.6.2 Fuzzy Rules and Fuzzy Graphs................................... 12.6.3 Fuzzy Graph Operations............................................... 12.6.4 Fuzzy Flybrid Systems................................................. 12.7 Summary...................................................................................... Exercises.................................................................................................... References................................................................................................. 363 365 367 369 369 371 373 Machine Learning ................................................................................. 13.1 Introduction................................................................................. 13.2 Types of Machine Learning........................................................ 13.3 Discipline of Machine Learning.................................................. 13.4 Learning Model .......................................................................... 13.5 Classes of Learning...................................................................... 13.5.1 Supervised Learning.................................................... 13.5.2 Unsupervised Learning............................................... 13.6 Inductive Learning...................................................................... 13.6.1 Argument-Based Learning........................................... 13.6.2 Mutual Online Concept Learning............................... 13.6.3 Single-Agent Online Concept Learning.................... 13.6.4 Propositional and Relational Learning...................... 13.6.5 LearningThrough Decision Trees.......................... 13.7 Discovery-Based Learning.......................................................... 13.8 Reinforcement Learning ............................................................. 13.8.1 Some Functions in Reinforcement Learning.............. 13.8.2 Supervised Versus Reinforcement Learning.............. 13.9 Learning and Reasoning by Analogy........................................ 13.10 A Framework of Symbol-Based Learning.................................. 13.11 Explanation-Based Learning ...................................................... 13.12 Machine Learning Applications.................................................. 13.13 Basic Research Problems in Machines Learning...................... 13.14 Summary..................................................................................... Exercises................................................................................................... References................................................................................................. 375 375 377 379 382 383 383 384 384 387 389 391 392 393 396 398 399 400 401 405 406 408 409 410 412 413 Statistical Learning Theory................................................................... 14.1 Introduction................................................................................. 14.2 Classification............................................................................... 14.3 Support Vector Machines .......................................................... 14.3.1 Learning Pattem Recognition from Examples........... 14.3.2 Maximum Margin Training Algorithm...................... 14.4 Predicting Structured Objects Using SVM............................... 14.5 Working of Structural SVMs...................................................... 415 415 416 418 419 421 422 424 Contents 15 xxi 14.6 к-Nearest Neighbor Method.......................................................... 14.6.1 к-NN Search Algorithm.............................................. 14.7 Naive Bayes Classifiers................................................................. 14.8 Artificial Neural Networks............................................................ 14.8.1 Error-Correction Rules................................................. 14.8.2 Boltzmann Learning..................................................... 14.8.3 Hebbian Rule................................................................. 14.8.4 Competitive Learning Rules........................................ 14.8.5 Deep Learning............................................................... 14.9 Instance-Based Learning............................................................... 14.9.1 Learning Task............................................................... 14.9.2 IBL Algorithm............................................................... 14.10 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 425 426 428 430 433 434 435 435 436 437 437 438 439 441 442 Automated Planning................................................................................. 15.1 Introduction.................................................................................... 15.2 Automated Planning...................................................................... 15.3 The Basic Planning Problem........................................................ 15.3.1 The Classical Planning Problem................................... 15.3.2 Agent Types................................................................... 15.4 Forward Planning.......................................................................... 15.5 Partial-Order Planning................................................................... 15.6 Planning Languages..................................................................... 15.6.1 A General Planning Language..................................... 15.6.2 The Operation of STRIPS............................................ 15.6.3 Search Strategy............................................................... 15.7 Planning with Propositional Logic.............................................. 15.7.1 Encoding Action Descriptions..................................... 15.7.2 Analysis.......................................................................... 15.8 Planning Graphs............................................................................ 15.9 Hierarchical Task Network Planning.......................................... 15.10 Multiagent Planning Systems........................................................ 15.11 Multiagent Planning Techniques ................................................. 15.11.1 Goal and Task Allocation............................................ 15.11.2 Goal and Task Refinement.......................................... 15.11.3 Decentralized Planning................................................. 15.11.4 Coordination After Planning........................................ 15.12 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 445 445 447 448 449 450 453 454 455 456 457 458 458 460 461 461 462 464 465 466 466 466 467 467 469 470 χχη 16 17 Contents Intelligent Agents.................................................................................... 16.1 Introduction................................................................................. 16.2 Classification of Agents............................................................... 16.3 Multiagent Systems...................................................................... 16.3.1 Single-Agent Framework............................................. 16.3.2 Multiagent Framework............................................... 16.3.3 Multiagent Interactions............................................... 16.4 Basic Architecture of Agent System........................................... 16.5 Agents’ Coordination ................................................................. 16.5.1 Sharing Among Cooperative Agents......................... 16.5.2 Static Coalition Formation........................................... 16.5.3 Dynamic Coalition Formation.................................... 16.5.4 Iterated Prisoner’s Dilemma Coalition Model........... 16.5.5 Coalition Algorithm.................................................... 16.6 Agent-Based Approach to Software Engineering...................... 16.7 Agents that Buy and Sell............................................................. 16.8 Modeling Agents as Decision Maker......................................... 16.8.1 Issues in Mental Level Modeling............................... 16.8.2 Model Structure........................................................... 16.8.3 Preferences.................................................................... 16.8.4 Decision Criteria.......................................................... 16.9 Agent Communication Languages............................................. 16.9.1 Semantics of Agent Programs.................................... 16.9.2 Description Language for Interactive Agents........... 16.10 Mobile Agents............................................................................ 16.11 Social Level View of Multiagents............................................. 16.12 Summary...................................................................................... Exercises................................................................................................... References................................................................................................. 471 471 472 475 476 476 477 479 480 481 482 482 483 485 486 487 488 489 489 492 493 493 495 497 499 500 502 504 504 Data Mining............................................................................................ 17.1 Introduction................................................................................. 17.2 Perspectives of Data Mining...................................................... 17.3 Goals of Data Mining................................................................. 17.4 Evolution of Data Mining Algorithms...................................... 17.4.1 Transactions Data........................................................ 17.4.2 Data Streams............................................................... 17.4.3 Representation of Text-Based Data........................... 17.5 Classes of Data Mining Algorithms........................................... 17.5.1 Prediction Methods...................................................... 17.5.2 Clustering..................................................................... 17.5.3 Association Rules........................................................ 507 507 509 511 512 513 514 514 515 515 518 519 Contents 18 xxiii 17.6 Data Clustering and Cluster Analysis.......................................... 17.6.1 Applications of Clustering............................................ 17.6.2 General Utilities of Clustering..................................... 17.6.3 Traditional Clustering Methods................................... 17.6.4 Clustering Process ........................................................ 17.6.5 Pattern Representation and Feature Extraction......... 17.7 Clustering Algorithms................................................................... 17.7.1 Similarity Measures ..................................................... 17.7.2 Nearest Neighbor Clustering........................................ 17.7.3 Partitional Algorithms................................................... 17.8 Comparison of Clustering Techniques........................................ 17.9 Classification................................................................................. 17.10 Association Rule Mining............................................................... 17.11 Sequential Pattern Mining Algorithms........................................ 17.11.1 Problem Statement........................................................ 17.11.2 Notations for Sequential Pattem Mining..................... 17.11.3 Typical Sequential Pattem Mining.............................. 17.11.4 Apriori-Based Algorithm.............................................. 17.12 Scientific Applications in Data Mining........................................ 17.13 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 519 521 522 523 523 525 526 527 528 529 531 534 537 541 542 542 543 544 549 551 554 555 Information Retrieval................................................................................ 18.1 Introduction.................................................................................... 18.2 Retrieval Strategies........................................................................ 18.3 Boolean Model of IR System........................................................ 18.4 Vector Space Model...................................................................... 18.5 Indexing........................................................................................... 18.5.1 Index Construction........................................................ 18.5.2 Index Maintenance........................................................ 18.6 Probabilistic Retrieval Model........................................................ 18.7 Fuzzy Logic-Based IR................................................................... 18.8 Concept-Based IR........................................................................... 18.8.1 Concept-Based Indexing............................................... 18.8.2 Retrieval Algorithms...................................................... 18.9 Automatic Query Expansion in IR............................................... 18.9.1 Working of AQE.......................................................... 18.9.2 Related Techniques for Query Processing................... 18.10 Using Bayesian Networks for IR................................................. 18.10.1 Representation of Document and Query..................... 18.10.2 Bayes Probabilistic Inference Model.......................... 557 557 560 561 563 565 565 568 569 570 574 575 578 579 583 585 587 587 588 XXIV 19 Contents 18.10.3 Bayes Inference Algorithm........................................... 18.10.4 Representing Dependent Topics.................................... 18.11 Semantic IR on the Web................................................................. 18.12 Distributed IR.................................................................................... 18.13 Summary........................................................................................... Exercises......................................................................................................... References....................................................................................................... 589 592 592 595 597 599 601 Natural Language Processing................................................................... 19.1 Introduction...................................................................................... 19.2 Progress in NLP............................................................................... 19.3 Applications of NLP........................................................................ 19.4 Components of Natural Language Processing............................. 19.4.1 Syntax Analysis.............................................................. 19.4.2 Semantic Analysis ......................................................... 19.4.3 Discourse Analysis......................................................... 19.5 Grammars........................................................................................... 19.5.1 Phrase Structure.............................................................. 19.5.2 Phrase Structure Grammars........................................... 19.6 Classification of Grammars............................................................ 19.6.1 Chomsky Hierarchy of Grammars............................... 19.6.2 Transformational Grammars ......................................... 19.6.3 Ambiguous Grammars .................................................. 19.7 Prepositions in Applications........................................................... 19.8 Natural Language Parsing.............................................................. 19.8.1 Parsing with CFGs......................................................... 19.8.2 Sentence-Level Constructions...................................... 19.8.3 Top-Down Parsing......................................................... 19.8.4 Probabilistic Parsing....................................................... 19.9 Information Extraction..................................................................... 19.9.1 Document Preprocessing............................................... 19.9.2 Syntactic Parsing and Semantic Interpretation............ 19.9.3 Discourse Analysis......................................................... 19.9.4 Output Template Generation........................................ 19.10 NL-Question Answering................................................................ 19.10.1 Data Redundancy Based Approach............................ 19.10.2 Structured Descriptive Grammar-Based QA.............. 19.11 Commonsense-Based Interfaces.................................................... 19.11.1 Commonsense Thinking............................................... 19.11.2 Components of Commonsense Reasoning................. 19.11.3 Representation Structures............................................. 603 603 606 608 609 609 611 611 612 613 613 616 616 617 619 620 621 622 624 625 627 630 630 631 632 633 633 634 635 636 638 638 640 Contents xxv 19.12 Tools for NLP............................................................................... 19.12.1 NLTK............................................................................. 19.12.2 NLTK Examples............................................................ 19.13 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 642 642 643 645 647 649 20 AutomaticSpeech Recognition................................................................ 20.1 Introduction.................................................................................... 20.2 Automatic Speech Recognition Resources................................... 20.3 Voice Web...................................................................................... 20.4 Speech Recognition Algorithms................................................... 20.5 Hypothesis Search in ASR............................................................ 20.5.1 Lexicon.......................................................................... 20.5.2 Language Model............................................................ 20.5.3 Acoustic Models............................................................ 20.6 Automatic Speech Recognition Tools.......................................... 20.6.1 Automatic Speech RecognitionEngine......................... 20.6.2 Tools for ASR............................................................... 20.7 Summary........................................................................................ Exercises...................................................................................................... References.................................................................................................... 651 651 653 654 656 658 658 658 659 662 663 664 666 667 668 21 Machine Vision ........................................................................................ 21.1 Introduction.................................................................................... 21.2 Machine Vision Applications........................................................ 21.3 Basic Principles of Vision............................................................ 21.4 Cognition and Classification ........................................................ 21.5 From Image-to-Scene................................................................... 21.5.1 Inversion by Fixing Scene Parameters....................... 21.5.2 Inversion by Restricting theProblemDomain.............. 21.5.3 Inversion by AcquiringAdditionalImages................... 21.6 Machine Vision Techniques........................................................... 21.6.1 Low-Level Vision.......................................................... 21.6.2 Local Edge Detection................................................... 21.6.3 Middle-Level Vision...................................................... 21.6.4 High-Level Vision........................................................ 21.7 Indexing and Geometric Hashing.................................................. 21.8 Object Representation and Tracking............................................ 21.9 Feature Selection and Object Detection ..................................... 21.9.1 Object Detection.............................................................. 21.10 Supervised Learning for Object Detection................................... 669 669 671 672 675 677 678 678 679 680 680 681 683 685 687 689 692 694 696 xxvi Contents 21.11 Axioms of Vision......................................................................... 21.11.1 Mathematical Axioms.................................................. 21.11.2 Source Axioms............................................................. 21.11.3 Model Axioms............................................................. 21.11.4 Construct Axioms......................................................... 21.12 Computer Vision Tools............................................................... 21.13 Summary...................................................................................... Exercises.................................................................................................... References.................................................................................................. 69g 699 699 700 700 701 703 705 706 Further Readings............................................................................................ 707 Index.................................................................................................................. 709 K. R. Chowdhary Fundamentals of Artificial Intelligence Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AT
adam_txt	Contents 1 Introducing Artificial Intelligence. 1.1 Introduction. 1.2 The Turing Test. 1.3 Goals of AI. 1.4 Roots of AI. 1.4.1 Philosophy. 1.4.2 Logic and Mathematics. 1.4.3 Computation. 1.4.4 Psychology and Cognitive Science. 1.4.5 Biology and Neuroscience. 1.4.6 Evolution. 1.5 Artificial Consciousness. 1.6 Techniques Used in AI. 1.7 Sub-fields of AI. 1.7.1 Speech Processing. 1.7.2 Natural Language Processing. 1.7.3 Planning. 1.7.4 Engineering and Expert Systems. 1.7.5 Fuzzy Systems. 1.7.6 Models of Brain and Evolution. 1.8 Perception, Understanding, and Action. 1.9 Physical Symbol System Hypothesis. 1.9.1 Formal System. 1.9.2 Symbols and Physical Symbol Systems. 1.9.3 Formal Logic. 1.9.4 The Stored Program Concept. 1.10 Considerations for KnowledgeRepresentation. 1.10.1 Defining the Knowledge. 1.10.2 Objective of Knowledge Representation. 1 1 3 4 5 6 6 7 7 8 8 9 9 10 11 11 12 12 13 13 14 14 15 15 16 16 16 17 17 xiii xiv 2 3 Contents 1.10.3 Requirements of a Knowledge Representation. 1.10.4 Practical Aspects of Representations . 1.10.5 Components of a Representation. 1.11 Knowledge Representation Using Natural Language. 1.12 Summary. Exercises. References. 18 18 19 20 20 22 23 Logic 2.1 2.2 2.3 2.4 and Reasoning Patterns. Introduction. Argumentation Theory. Role of Knowledge. Propositional Logic. 2.4.1 Interpretation of Formulas. 2.4.2 Logical Consequence. 2.4.3 Syntax and Semantics of an Expression. 2.4.4 Semantic Tableau. 2.5 Reasoning Patterns. 2.5.1 Rule-Based Reasoning . 2.5.2 Model-Based Reasoning. 2.6 Proof Methods. 2.6.1 Normal Forms. 2.6.2 Resolution. 2.6.3 Properties of Inference Rules. 2.7 Nonmonotonic Reasoning. 2.8 Hilbert and the Axiomatic Approach. 2.8.1 Roots and Early Stages. 2.8.2 Axiomàtics and Formalism. 2.9 Summary. Exercises. References. 25 25 27 28 28 31 32 33 33 35 38 38 39 40 40 41 42 43 44 45 47 48 49 First Order Predicate Logic. 3.1 Introduction. 3.2 Representation in Predicate Logic . 3.3 Syntax and Semantics. 3.4 Conversion to Clausal Form . 3.5 Substitutions and Unification. 3.5.1 Composition of Substitutions. 3.5.2 Unification. 3.6 Resolution Principle. 3.6.1 Theorem Proving Formalism. 3.6.2 Proof by Resolution. 3.7 Complexity of Resolution Proof. 51 51 52 55 57 59 60 61 62 64 64 65 Contents XV 3.8 Interpretation and Inferences. 3.8.1 Herbrand’s Universe. 3.8.2 Herbrand’s Theorem. 3.8.3 The Procedural Interpretation. 3.9 Most General Unifiers. 3.9.1 Lifting. 3.9.2 Unification Algorithm. 3.10 Unfounded Sets . 3.11 Summary. Exercises. References. 66 68 71 72 76 78 79 81 83 84 88 4 Rule Based Reasoning. 4.1 Introduction. 4.2 An Overview of RBS. 4.3 Forward Chaining. 4.3.1 Forward Chaining Algorithm. 4.3.2 Conflict Resolution. 4.3.3 Efficiency in Rule Selection. 4.3.4 Complexity of Preconditions. 4.4 Backward Chaining. 4.4.1 Backward Chaining Algorithm. 4.4.2 Goal Determination. 4.5 Forward Versus Backward Chaining . 4.6 Typical RB System. 4.7 Other Systems of Reasoning. 4.7.1 Model-Based Systems. 4.7.2 Case-Based Reasoning. 4.8 Summary. Exercises. References. 89 89 91 93 93 95 97 98 98 99 100 100 102 102 103 104 105 106 109 5 Logic 5.1 5.2 5.3 5.4 Ill Ill 112 114 116 117 119 120 121 122 124 129 5.5 5.6 5.7 Programming and Prolog. Introduction. Logic Programming. Interpretation of Horn Clauses in Rule- Chaining. Logic Versus Control. 5.4.1 Data Structures. 5.4.2 Procedure-Call Execution. 5.4.3 Backward Versus Forward Reasoning. 5.4.4 Path Finding Algorithm. Expressing Control Information. Running Simple Programs. Some Built-In Predicates. xvi 6 7 Contents 5.8 Recursive Programming. 5.9 List Manipulation. 5.10 Arithmetic Expressions. 5.11 Backtracking, Cuts and Negation. 5.12 Efficiency Considerations for PrologPrograms. 5.13 Summary. Exercises. References. 130 132 135 135 137 138 139 141 Real-World Knowledge Representation and Reasoning. 6.1 Introduction. 6.2 Taxonomic Reasoning. 6.3 Techniques for Commonsense Reasoning. 6.4 Ontologies. 6.5 Ontology Structures. 6.5.1 Language and Reasoning. 6.5.2 Levels of Ontologies. 6.5.3 WordNet. 6.5.4 Axioms and First-Order Logic. 6.5.5 Sowa’s Ontology. 6.6 Reasoning Using Ontologies. 6.6.1 Categories and Objects. 6.6.2 Physical Decomposition of Categories. 6.6.3 Measurements. 6.6.4 Object-Oriented Analysis. 6.7 Ontological Engineering. 6.8 Situation Calculus. 6.8.1 Action, Situation, and Objects. 6.8.2 Formalism. 6.8.3 Formalizing the Notions of Context. 6.9 Nonmonotonic Reasoning. 6.10 Default Reasoning. 6.10.1 Notion of a Default. 6.10.2 The Syntax of Default Logic. 6.10.3 Algorithm for Default Reasoning. 6.11 Summary. Exercises. References. 143 143 144 147 148 150 151 152 153 154 154 156 156 157 157 157 158 159 159 160 163 165 166 168 169 170 172 172 176 Networks-Based Representation. 7.1 Introduction. 7.2 Semantic Networks. 7.2.1 Syntax and Semantics of Semantics Networks. 7.2.2 Human Knowledge Creation. 179 179 180 182 184 Contents 8 xvii 7.2.3 Semantic Nets and Natural LanguageProcessing . 7.2.4 Performance. 7.3 Conceptual Graphs. 7.4 Frames and Reasoning. 7.4.1 Inheritance Hierarchies. 7.4.2 Slots Terminology . 7.4.3 Frame Languages. 7.4.4 Case Study. 7.5 Description Logic. 7.5.1 Definitions and Sentence Structures. 7.5.2 Concept Language. 7.5.3 Architecture for Ջ if Knowledge Representation. 7.5.4 Value Restrictions. 7.5.5 Reasoning and Inferences. 7.6 Conceptual Dependencies. 7.6.1 The Parser. 7.6.2 Conceptual Dependency and Inferences. 7.6.3 Scripts. 7.6.4 Conceptual Dependency Versus Semantic Nets. 7.7 Summary. Exercises. References. 184 185 185 188 189 190 191 192 195 196 197 State Space Search. 8.1 Introduction. 8.2 Representation of Search. 8.3 Graph Search Basics. 8.4 Complexities of State-Space Search. 8.5 Uninformed Search. 8.5.1 Breadth-First Search. 8.5.2 Depth-First Search. 8.5.3 Analysis of BFS and DFS. 8.5.4 Depth-First Iterative Deepening Search. 8.5.5 Bidirectional Search. 8.6 Memory Requirements for Search Algorithms. 8.6.1 Depth-First Searches. 8.6.2 Breadth-First Searches. 8.7 Problem Formulation for Search. 8.8 Summary. Exercises. References. 217 217 218 219 220 222 222 224 225 227 228 229 229 230 230 232 232 236 201 202 203 204 207 209 210 211 212 213 215 xviii Contents 9 Heuristic Search. 9.1 Introduction. 9.2 Heuristic Approach. 9.3 Hill-Climbing Methods. 9.4 Best-First Search. 9.4.1 GBFS Algorithm. 9.4.2 Analysis of Best-First Search. 9.5 Heuristic Determination of Minimum Cost Paths . 9.5.1 Search Algorithm A. 9.5.2 The Evaluation Function. 9.5.3 Analysis of A Search. 9.5.4 Optimality of Algorithm A*. 9.6 Comparison of Heuristics Approaches. 9.7 Simulated Annealing. 9.8 Genetic Algorithms. 9.8.1 Exploring Different Structures. 9.8.2 Process of Innovation in Human. 9.8.3 Mutation Operator . 9.8.4 GA Applications. 9.9 Summary. Exercises. References. 239 239 241 242 244 245 247 249 249 251 253 254 254 256 259 260 261 261 261 263 265 271 10 Constraint Satisfaction Problems. 10.1 Introduction. 10.2 CSP Applications. 10.3 Representation of CSP. 10.3.1 Constraints in CSP. 10.3.2 Variables in CSP. 10.4 Solving a CSP. 10.4.1 Synthesizing the Constraints. 10.4.2 An Extended Theory for Synthesizing. 10.5 Solution Approaches to CSPs . 10.6 CSP Algorithms. 10.6.1 Generate and Test. 10.6.2 Backtracking. 10.6.3 Efficiency Considerations . 10.7 Propagating of Constraints. 10.7.1 Forward Checking. 10.7.2 Degree of Heuristics. 10.8 Cryptarithmetics. 10.9 Theoretical Aspects of CSPs. 273 273 274 276 277 279 280 281 283 285 287 288 288 292 293 294 294 295 298 Contents XIX 10.10 Summary. Exercises. References. 299 299 302 11 Adversarial Search and Game Theory. 11.1 Introduction. 11.2 Classification of Games. 11.3 Game Playing Strategy. 11.4 Two-Person Zero-Sum Games. 11.5 The Prisoner’s Dilemma. 11.6 Two-Player Game Strategies. 11.7 Games of Perfect Information. 11.8 Games of Imperfect Information . 11.9 Nash Arbitration Scheme. 11.10 n-Person Games. 11.11 Representation of Two-Player Games. 11.12 Minimax Search. 11.13 Tic-tac-toe Game Analysis. 11.14 Alpha-Beta Search . 11.14.1 Complexities Analysis of Alpha-Beta. 11.14.2 Improving the Efficiency of Alpha-Beta. 11.15 Sponsored Search. 11.16 Playing Chess with Computer. 11.17 Summary. Exercises. References. 303 303 305 306 307 308 310 312 312 314 316 317 318 321 324 326 327 328 329 329 330 335 12 Reasoning in Uncertain Environments. 12.1 Introduction. 12.2 Foundations of Probability Theory. 12.3 Conditional Probability and Bayes Theorem. 12.4 Bayesian Networks. 12.4.1 Constructing a Bayesian Network. 12.4.2 Bayesian Network for Chain of Variables . 12.4.3 Independence of Variables . 12.4.4 Propagation in Bayesian Belief Networks. 12.4.5 Causality and Independence. 12.4.6 Hidden Markov Models. 12.4.7 Construction Process of Bayesian Networks. 12.5 Dempster-Shafer Theory of Evidence. 12.5.1 Dempster-Shafer Rule of Combination . 12.5.2 Dempster-Shafer Versus Bayes Theory. 12.6 Fuzzy Sets, Fuzzy Logic, and Fuzzy Inferences. 337 337 339 340 344 344 345 347 348 351 353 354 356 357 358 361 XX 13 14 Contents 12.6.1 Fuzzy Composition Relation. 12.6.2 Fuzzy Rules and Fuzzy Graphs. 12.6.3 Fuzzy Graph Operations. 12.6.4 Fuzzy Flybrid Systems. 12.7 Summary. Exercises. References. 363 365 367 369 369 371 373 Machine Learning . 13.1 Introduction. 13.2 Types of Machine Learning. 13.3 Discipline of Machine Learning. 13.4 Learning Model . 13.5 Classes of Learning. 13.5.1 Supervised Learning. 13.5.2 Unsupervised Learning. 13.6 Inductive Learning. 13.6.1 Argument-Based Learning. 13.6.2 Mutual Online Concept Learning. 13.6.3 Single-Agent Online Concept Learning. 13.6.4 Propositional and Relational Learning. 13.6.5 LearningThrough Decision Trees. 13.7 Discovery-Based Learning. 13.8 Reinforcement Learning . 13.8.1 Some Functions in Reinforcement Learning. 13.8.2 Supervised Versus Reinforcement Learning. 13.9 Learning and Reasoning by Analogy. 13.10 A Framework of Symbol-Based Learning. 13.11 Explanation-Based Learning . 13.12 Machine Learning Applications. 13.13 Basic Research Problems in Machines Learning. 13.14 Summary. Exercises. References. 375 375 377 379 382 383 383 384 384 387 389 391 392 393 396 398 399 400 401 405 406 408 409 410 412 413 Statistical Learning Theory. 14.1 Introduction. 14.2 Classification. 14.3 Support Vector Machines . 14.3.1 Learning Pattem Recognition from Examples. 14.3.2 Maximum Margin Training Algorithm. 14.4 Predicting Structured Objects Using SVM. 14.5 Working of Structural SVMs. 415 415 416 418 419 421 422 424 Contents 15 xxi 14.6 к-Nearest Neighbor Method. 14.6.1 к-NN Search Algorithm. 14.7 Naive Bayes Classifiers. 14.8 Artificial Neural Networks. 14.8.1 Error-Correction Rules. 14.8.2 Boltzmann Learning. 14.8.3 Hebbian Rule. 14.8.4 Competitive Learning Rules. 14.8.5 Deep Learning. 14.9 Instance-Based Learning. 14.9.1 Learning Task. 14.9.2 IBL Algorithm. 14.10 Summary. Exercises. References. 425 426 428 430 433 434 435 435 436 437 437 438 439 441 442 Automated Planning. 15.1 Introduction. 15.2 Automated Planning. 15.3 The Basic Planning Problem. 15.3.1 The Classical Planning Problem. 15.3.2 Agent Types. 15.4 Forward Planning. 15.5 Partial-Order Planning. 15.6 Planning Languages. 15.6.1 A General Planning Language. 15.6.2 The Operation of STRIPS. 15.6.3 Search Strategy. 15.7 Planning with Propositional Logic. 15.7.1 Encoding Action Descriptions. 15.7.2 Analysis. 15.8 Planning Graphs. 15.9 Hierarchical Task Network Planning. 15.10 Multiagent Planning Systems. 15.11 Multiagent Planning Techniques . 15.11.1 Goal and Task Allocation. 15.11.2 Goal and Task Refinement. 15.11.3 Decentralized Planning. 15.11.4 Coordination After Planning. 15.12 Summary. Exercises. References. 445 445 447 448 449 450 453 454 455 456 457 458 458 460 461 461 462 464 465 466 466 466 467 467 469 470 χχη 16 17 Contents Intelligent Agents. 16.1 Introduction. 16.2 Classification of Agents. 16.3 Multiagent Systems. 16.3.1 Single-Agent Framework. 16.3.2 Multiagent Framework. 16.3.3 Multiagent Interactions. 16.4 Basic Architecture of Agent System. 16.5 Agents’ Coordination . 16.5.1 Sharing Among Cooperative Agents. 16.5.2 Static Coalition Formation. 16.5.3 Dynamic Coalition Formation. 16.5.4 Iterated Prisoner’s Dilemma Coalition Model. 16.5.5 Coalition Algorithm. 16.6 Agent-Based Approach to Software Engineering. 16.7 Agents that Buy and Sell. 16.8 Modeling Agents as Decision Maker. 16.8.1 Issues in Mental Level Modeling. 16.8.2 Model Structure. 16.8.3 Preferences. 16.8.4 Decision Criteria. 16.9 Agent Communication Languages. 16.9.1 Semantics of Agent Programs. 16.9.2 Description Language for Interactive Agents. 16.10 Mobile Agents. 16.11 Social Level View of Multiagents. 16.12 Summary. Exercises. References. 471 471 472 475 476 476 477 479 480 481 482 482 483 485 486 487 488 489 489 492 493 493 495 497 499 500 502 504 504 Data Mining. 17.1 Introduction. 17.2 Perspectives of Data Mining. 17.3 Goals of Data Mining. 17.4 Evolution of Data Mining Algorithms. 17.4.1 Transactions Data. 17.4.2 Data Streams. 17.4.3 Representation of Text-Based Data. 17.5 Classes of Data Mining Algorithms. 17.5.1 Prediction Methods. 17.5.2 Clustering. 17.5.3 Association Rules. 507 507 509 511 512 513 514 514 515 515 518 519 Contents 18 xxiii 17.6 Data Clustering and Cluster Analysis. 17.6.1 Applications of Clustering. 17.6.2 General Utilities of Clustering. 17.6.3 Traditional Clustering Methods. 17.6.4 Clustering Process . 17.6.5 Pattern Representation and Feature Extraction. 17.7 Clustering Algorithms. 17.7.1 Similarity Measures . 17.7.2 Nearest Neighbor Clustering. 17.7.3 Partitional Algorithms. 17.8 Comparison of Clustering Techniques. 17.9 Classification. 17.10 Association Rule Mining. 17.11 Sequential Pattern Mining Algorithms. 17.11.1 Problem Statement. 17.11.2 Notations for Sequential Pattem Mining. 17.11.3 Typical Sequential Pattem Mining. 17.11.4 Apriori-Based Algorithm. 17.12 Scientific Applications in Data Mining. 17.13 Summary. Exercises. References. 519 521 522 523 523 525 526 527 528 529 531 534 537 541 542 542 543 544 549 551 554 555 Information Retrieval. 18.1 Introduction. 18.2 Retrieval Strategies. 18.3 Boolean Model of IR System. 18.4 Vector Space Model. 18.5 Indexing. 18.5.1 Index Construction. 18.5.2 Index Maintenance. 18.6 Probabilistic Retrieval Model. 18.7 Fuzzy Logic-Based IR. 18.8 Concept-Based IR. 18.8.1 Concept-Based Indexing. 18.8.2 Retrieval Algorithms. 18.9 Automatic Query Expansion in IR. 18.9.1 Working of AQE. 18.9.2 Related Techniques for Query Processing. 18.10 Using Bayesian Networks for IR. 18.10.1 Representation of Document and Query. 18.10.2 Bayes Probabilistic Inference Model. 557 557 560 561 563 565 565 568 569 570 574 575 578 579 583 585 587 587 588 XXIV 19 Contents 18.10.3 Bayes Inference Algorithm. 18.10.4 Representing Dependent Topics. 18.11 Semantic IR on the Web. 18.12 Distributed IR. 18.13 Summary. Exercises. References. 589 592 592 595 597 599 601 Natural Language Processing. 19.1 Introduction. 19.2 Progress in NLP. 19.3 Applications of NLP. 19.4 Components of Natural Language Processing. 19.4.1 Syntax Analysis. 19.4.2 Semantic Analysis . 19.4.3 Discourse Analysis. 19.5 Grammars. 19.5.1 Phrase Structure. 19.5.2 Phrase Structure Grammars. 19.6 Classification of Grammars. 19.6.1 Chomsky Hierarchy of Grammars. 19.6.2 Transformational Grammars . 19.6.3 Ambiguous Grammars . 19.7 Prepositions in Applications. 19.8 Natural Language Parsing. 19.8.1 Parsing with CFGs. 19.8.2 Sentence-Level Constructions. 19.8.3 Top-Down Parsing. 19.8.4 Probabilistic Parsing. 19.9 Information Extraction. 19.9.1 Document Preprocessing. 19.9.2 Syntactic Parsing and Semantic Interpretation. 19.9.3 Discourse Analysis. 19.9.4 Output Template Generation. 19.10 NL-Question Answering. 19.10.1 Data Redundancy Based Approach. 19.10.2 Structured Descriptive Grammar-Based QA. 19.11 Commonsense-Based Interfaces. 19.11.1 Commonsense Thinking. 19.11.2 Components of Commonsense Reasoning. 19.11.3 Representation Structures. 603 603 606 608 609 609 611 611 612 613 613 616 616 617 619 620 621 622 624 625 627 630 630 631 632 633 633 634 635 636 638 638 640 Contents xxv 19.12 Tools for NLP. 19.12.1 NLTK. 19.12.2 NLTK Examples. 19.13 Summary. Exercises. References. 642 642 643 645 647 649 20 AutomaticSpeech Recognition. 20.1 Introduction. 20.2 Automatic Speech Recognition Resources. 20.3 Voice Web. 20.4 Speech Recognition Algorithms. 20.5 Hypothesis Search in ASR. 20.5.1 Lexicon. 20.5.2 Language Model. 20.5.3 Acoustic Models. 20.6 Automatic Speech Recognition Tools. 20.6.1 Automatic Speech RecognitionEngine. 20.6.2 Tools for ASR. 20.7 Summary. Exercises. References. 651 651 653 654 656 658 658 658 659 662 663 664 666 667 668 21 Machine Vision . 21.1 Introduction. 21.2 Machine Vision Applications. 21.3 Basic Principles of Vision. 21.4 Cognition and Classification . 21.5 From Image-to-Scene. 21.5.1 Inversion by Fixing Scene Parameters. 21.5.2 Inversion by Restricting theProblemDomain. 21.5.3 Inversion by AcquiringAdditionalImages. 21.6 Machine Vision Techniques. 21.6.1 Low-Level Vision. 21.6.2 Local Edge Detection. 21.6.3 Middle-Level Vision. 21.6.4 High-Level Vision. 21.7 Indexing and Geometric Hashing. 21.8 Object Representation and Tracking. 21.9 Feature Selection and Object Detection . 21.9.1 Object Detection. 21.10 Supervised Learning for Object Detection. 669 669 671 672 675 677 678 678 679 680 680 681 683 685 687 689 692 694 696 xxvi Contents 21.11 Axioms of Vision. 21.11.1 Mathematical Axioms. 21.11.2 Source Axioms. 21.11.3 Model Axioms. 21.11.4 Construct Axioms. 21.12 Computer Vision Tools. 21.13 Summary. Exercises. References. 69g 699 699 700 700 701 703 705 706 Further Readings. 707 Index. 709 K. R. Chowdhary Fundamentals of Artificial Intelligence Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AT
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