Introduction to artificial intelligence:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Reading, Mass. [u.a.]
Addison-Wesley
1987
|
| Ausgabe: | Reprinted with corr. |
| Schriftenreihe: | Addison-Wesley series in computer science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 701 S. Ill., graph. Darst. |
| ISBN: | 0201119455 |
Internformat
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| 245 | 1 | 0 | |a Introduction to artificial intelligence |c Eugene Charniak ; Drew McDermott |
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Datensatz im Suchindex
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| adam_text |
Introduction to
Artificial Intelligence
Eugene Charniak
Brown University
Drew McDermott
Yale University iechnisdte iiochschule Darmstadt
FACHBEREICH INFORM ATsK
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Standorti
TT
ADDISON-WESLEY PUBLISHING COMPANY
Reading, Massachusetts 9 Menlo Park, California • Don Mills, Ontario
Wokingham, England • Amsterdam • Sydney • Singapore • Tokyo • Mexico City
Bogota • Santiago • San Juan
Contents
1 AI and Internal Representation 1
1 1 Artificial Intelligence and the World 1
1 2 What Is Artificial Intelligence? 6
1 3 Representation in AI 8
1 4 Properties of Internal Representation 11
1 5 The Predicate Calculus 14
151 Predicates and Arguments 15
152 Connectives 16
153 Variables and Quantification 18
154 How to Use the Predicate Calculus 18
1 6 Other Kinds of Inference 21
1 7 Indexing, Pointers, and Alternative Notations 22
171 Indexing 24
172 The Isa Hierarchy 25
173 Slot-Assertion Notation 27
174 Frame Notation 28
1 8 References and Further Reading 28
Exercises 29
2 Lisp 33
2 1 Why Lisp? 33
2 2 Lisps 34
xii Contents
2 3 Typing at Lisp 34
2 4 Defining Programs 37
2 5 Basic Flow of Control in Lisp 39
2 6 Lisp Style 42
2 7 Atoms and Lists 44
2 8 Basic Debugging 48
2 9 Building Up List Structure 53
2 10 More on Predicates 56
2 11 Properties 58
2 12 Pointers, Cell Notation, and the Internals (Almost) of Lisp 59
2 13 Destructive Modification of Lists 65
2 14 The for Function 70
2 15 Recursion 72
2 16 Scope of Variables 73
2 17 Input/Output 76
2 18 Macros 78
2 19 References and Further Reading 80
Exercises 80
3 Vision 87
3 1 Introduction 87
3 2 Defining the Problem 89
3 3 Overview of the Solution 93
3 4 Early Processing 97
341 Gray-Level Image to Primal Sketch 99
342 Convolution with Gaussians (Optional) 101
343 Virtual Lines I l l
344 Stereo Disparity 113
345 Texture 120
346 Intrinsic Image 126
347 Cooperative Algorithms 131
348 Vertex Analysis and Line Labeling 137
3 5 Representing and Recognizing Scenes 143
351 Shape Description 144
352 Matching Shape Descriptions 150
353 Finding a Known Shape to Match Against 153
354 Describing a Seen Shape 155
Contents Xlll
3 6 References and Further Reading 159
Exercises 160
4 Parsing Language 169
4 1 Levels of Language 169
4 2 Expressing the Rules of Syntax 174
421 Why We Need Rules of Syntax 174
422 Diagraming Sentences 175
423 Why Do We Care about Sentence Structure? 177
424 Context-Free Grammars 179
425 Dictionaries and Features 183
426 Transformational Grammar (Optional) 188
4 3 Syntactic Parsing 194
431 Top-Down and Bottom-Up Parsing 194
432 Transition Network Parsers 197
433 Augmented Transition Networks (Optional) 206
434 Movement Rules in ATN Grammars (Optional) 210
4 4 Building an ATN Interpreter (Optional) 214
441A Non-Backtracking ATN Interpreter 215
442A Backtracking ATN Interpreter 218
443 Alternative Search Strategies 222
4 5 From Syntax to Semantics 223
451 The Interpretation of Definite Noun Phrases 223
452 Case Grammar and the Meaning of Verbs 230
4 6 When Semantics, When Syntax? 238
461 The Syntactic Use of Semantic Knowledge 238
462 The Organization of Parsing 241
4 7 References and Further Reading 245
Exercises 246
5 Search 255
5 1 Introduction 255
511 The Need for Guesswork 255
512 Search Problems 257
52A Search Algorithm 259
5 3 Goal Trees 270
531 Formal Definition 271
532 Searching Goal Trees 274
xiv Contents
533 Formalism Revisited 275
534 ATN Parsing as a Search Problem (Optional) 278
5 4 Game Trees (Optional) 281
541 Game Trees as Goal Trees 281
542 Minimax Search 286
543 Actual Game Playing 290
5 5 Avoiding Repeated States 294
5 6 Transition-oriented State Representations (Optional) 297
5 7 GPS 300
5 8 Continuous Optimization (Optional) 306
5 9 Summary 309
5 10 References and Further Reading 310
Exercises 311
6 Logic and Deduction 319
6 1 Introduction 319
6 2 Using Predicate Calculus 321
621 Syntax and Semantics 321
622 Some Abstract Representations 323
623 Quantifiers and Axioms 333
624 Encoding Facts as Predicate Calculus 337
625 Discussion 343
6 3 Deduction as Search 344
631 Forward Chaining and Unification 345
632 Skolemization 349
633 Backward Chaining 351
634 Goal Trees for Backward Chaining 353
6 4 Applications of Theorem Proving 360
641 Mathematical Theorem Proving 361
642 Deductive Retrieval and Logic Programming 365
6 5 Advanced Topics in Representation 369
651 Nonmonotonic Reasoning 369
652 Using X-Expressions as Descriptions (Optional) 371
653 Modal and Intensional Logics (Optional) 375
6 6 Complete Resolution (Optional) 378
661 The General Resolution Rule 379
662 Search Algorithms for Resolution 382
Contents XV
6 7 References and Further Reading 385
Exercises 386
7 Memory Organization and Deduction 393
7 1 The Importance of Memory Organization 393
7 2 Approaches to Memory Organization 396
721 Indexing Predicate-Calculus Assertions 396
722 Associative Networks 400
723 Property Inheritance 405
7 3 Data Dependencies 411
7 4 Reasoning Involving Time 416
741 The Situation Calculus 417
742 Temporal System Analysis 420
743 Time-Map Management 429
7 5 Spatial Reasoning 433
7 6 Rule-Based Programming 437
7 7 References and Further Reading 440
Exercises 442
8 Abduction, Uncertainty and Expert Systems 453
8 1 What Is Abduction? 453
811 Abduction and Causation 453
812 Abduction and Evidence 454
813 Expert Systems 455
8 2 Statistics in Abduction 457
821 Basic Definitions 457
822 Bayes's Theorem 460
823 The Problem of Multiple Symptoms 461
8 3 The Mycin Program for Infectious Diseases 465
8 4 Search Considerations in Abduction 468
841 Search Strategy in Mycin 468
842 Bottom-Up Abduction 469
843 Search in Caduceus 471
8 5 Multiple Diseases 471
851 Multiple Diseases According to Bayes 471
852 Heuristic Techniques 472
8 6 Caduceus 474
8 7 Bayesian Inference Networks 477
XVi Contents
8 8 Still More Complicated Cases 480
8 9 References and Further Reading 482
Exercises 482
9 Managing Plans of Action 485
9 1 Introduction 485
92A Basic Plan Interpreter 489
9 3 Planning Decisions 499
931 Anticipating Protection Violations 499
932 Choosing Objects to Use 511
933 Temporally Restricted Goals 512
934 Planning by Searching through Situations 514
935 Shallow Reasoning about Plans 518
936 Decision Theory 519
9 4 Execution Monitoring and Replanning 524
9 5 Domains of Application 527
951 Robot Motion Planning 527
952 Game Playing 535
9 6 References and Further Reading 542
Exercises 543
10 Language Comprehension 555
10 1 Story Comprehension as Abduction 555
10 2 Determining Motivation 557
10 2 1 Motivation Analysis = Plan Synthesis in Reverse 557
10 2 2 Deciding Between Motivations 560
10 2 3 When to Stop 566
10 3 Generalizing the Model 567
10 3 1 Abductive Projection 567
10 3 2 Understanding Obstacles to Plans 569
10 3 3 Subsumption Goals and repeat-until 572
10 4 Details of Motivation Analysis (Optional) 573
10 4 1 Abductive Matching 573
10 4 2 Finding Possible Motivations 578
10 5 Speech Acts and Conversation 581
10 5 1 Speech Acts in Problem Solving 584
10 5 2 The Recognition of Speech Acts 586
10 5 3 Conversations 589
Contents XVll
10 6 Disambiguation of Language 591
10 6 1 Referential Ambiguity and Context 592
10 6 2 Conversation and Reference 597
10 6 3 Word Sense Disambiguation 598
10 7 Where We Have Been, and Where We Are Going 601
10 8 References and Further Reading 603
Exercises 604
11 Learning 609
11 1 Introduction 609
11 2 Learning as Induction , 610
11 2 1 The Empiricist Algorithm 611
11 2 2 Generalization and Specialization 614
11 2 3 Matching 621
11 24A Matching Algorithm (Optional) 624
11 2 5 Analogy 626
11 2 6 Indexing for Learning 629
11 2 7 Assessment 633
11 3 Failure-driven Learning 635
11 4 Learning by Being Told 638
11 5 Learning by Exploration 642
11 6 Learning Language 650
11 6 1 An Outline of the Problem 650
11 6 2 Determining the Internal Representation 651
11 6 3 Learning Phrase-structure Rules 652
11 6 4 Learning Transformational Rules 654
11 7 References and Further Reading 659
Exercises 660
References 663
Index 687 |
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| spelling | Charniak, Eugene 1946- Verfasser (DE-588)1183007523 aut Introduction to artificial intelligence Eugene Charniak ; Drew McDermott Reprinted with corr. Reading, Mass. [u.a.] Addison-Wesley 1987 XVII, 701 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Addison-Wesley series in computer science Künstliche Intelligenz Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content Künstliche Intelligenz (DE-588)4033447-8 s DE-604 McDermott, Drew V. 1949- Verfasser (DE-588)124415350 aut HEBIS Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=002335088&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Introduction to artificial intelligence |
| title_auth | Introduction to artificial intelligence |
| title_exact_search | Introduction to artificial intelligence |
| title_full | Introduction to artificial intelligence Eugene Charniak ; Drew McDermott |
| title_fullStr | Introduction to artificial intelligence Eugene Charniak ; Drew McDermott |
| title_full_unstemmed | Introduction to artificial intelligence Eugene Charniak ; Drew McDermott |
| title_short | Introduction to artificial intelligence |
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| topic_facet | Künstliche Intelligenz Artificial intelligence Einführung |
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