Avron Barr, Paul R Cohen, Edward A. Feigenbaum (Eds.), 1st edition, 4 volumes.
Department of Computer Science, Stanford University
Published in 1981-1982 by HeurisTech Press, William Kaufmann, Inc., in Stanford, California, USA.
The Handbook of AI, vol. . Avron Barr, Edward A. Feigenbaum, C. Roads (Eds.), 1981
The Handbook of AI, vol. . Avron Barr, Edward A. Feigenbaum (Eds.), 1982
The Handbook of AI, vol. . Paul R. Cohen, and Edward A. Feigenbaum (Eds.), 1982
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The Handbook of AI, vol. . Avron Barr, Paul R. Cohen, and Edward A. Feigenbaum (Eds.), 1989
This book has an editable web page on Open Library.
ISBN: 0-86576-004-7 (set)
Publisher: William Kaufmann, Inc.
Editors: Avron Barr, Paul R Cohen, Edward A. Feigenbaum
Open Access: Volume 1 | Volume 2 | Volume 3 | Volume 4
The Handbook of Artificial Intelligence - on academic.research.microsoft.com
Первый зарубежный (США) Справочник по искусственному интеллекту, неоднократно переиздававшийся и давно ставший библиографической редкостью, отражает состояние исследований в мировой науке на тот момент (1-2 издание, 1981-1989 гг.). Хотя некоторые материалы справочника требуют обновления, он не потерял своей актуальности и сейчас (смотри также Первый отечественный Справочник по искусственному интеллекту).
Началом разработки Справочника по искусственному интеллекту послужил семинар, который проводил весной 1975 г. в Стэнфордском университете (Stanford University) Эдвард Фейгенбаум (Edward Feigenbaum). Идея заключалась в том, что если бы каждый студент-слушатель написал 8-10 коротких статей, этого было бы достаточно, чтобы охватить большую часть этой области исследований. В подготовке материалов справочника и редактировании разделов приняли участие студенты и сотрудники Стэнфордского университета и других центров исследования ИИ, которые затем стали ведущими исследователями ИИ в университетах и корпорациях (смотри список составителей).
Подобно многим ранним проектам в области ИИ у Справочника были большие цели, но несколько наивный подход и общая вера в скорую разработку ИИ. 
Впоследствие (в 1989 г.) был издан 4 том Справочника, охватывающий дополнительно новые направления исследований в области ИИ.
Работа над Справочником позволила отобрать, систематизировать и обобщить материалы исследований по ИИ, проводившихся в тот момент, и является сжатой характеристикой накопленных знаний и опыта.
Справочник отражает историю исследований в области ИИ, формирования разделов практического использования ИИ, развития средств программирования для ИИ и может служить базовым пособием для студентов, учащихся специализированных курсов, аспирантов, программистов, изучающих вопросы, связанные с искусственным интеллектом. Кроме того, книга будет весьма полезна для разработчиков программного обеспечения для систем ИИ и профессионалов, желающих расширить рамки избранной ими специальности.
Ниже приведено выборочное описание некоторых материалов справочника.
CONTENTS
CONTENTS OF VOLUME I
List of Contributors / ix
Preface / xi
I. Introduction / 1
A. Artificial Intelligence / 3
B. The AI Handbook / 12
C. The AI literature / 14
II. Search / 19
A. Overview / 21
B. Problem representation / 32
1. State-space representation / 32
2. Problem-reduction representation / 36
3. Game trees / 43
C. Search methods / 46
1. Blind state-space search / 46
2. Blind AND/OR Graph search / 54
3. Heuristic state-space search / 58
a. Basic concepts in heuristic search / 58
b. A* — Optimal search for an optimal solution / 64
c. Relaxing the optimality requirement / 67
d. Bidirectional search / 72
4. Heuristic search of an AND/OR graph / 74
5. Game tree search / 84
a. Minimax procedure / 84
b. Alpha-beta pruning / 88
c. Heuristics in game tree search / 94
D. Sample search programs / 109
1. Logic Theorist / 109
2. General Problem Solver / 113
3. Gelernter's geometry theorem-proving machine / 119
4. Symbolic integration programs / 123
5. STRIPS / 128
6. ABSTRIPS / 135
III. Knowledge Representation / 141
A. Overview / 143
B. Survey of representation techniques / 153
C. Representation schemes / 160
1. Logic / 160
2. Procedural representations / 172
3. Semantic networks / 180
4. Production systems / 190
5. Direct (analogical) representations / 200
6. Semantic primitives / 207
7. Frames and scripts / 216
IV. Understanding Natural Language / 223
A. Overview / 225
B. Machine translation / 233
C. Grammars / 239
1. Formal grammars / 239
2. Transformational grammars / 245
3. Systemic grammar / 249
4. Case grammars / 252
D. Parsing / 256
1. Overview of parsing techniques / 256
2. Augmented transition networks / 263
3. The General Syntactic Processor / 268
E. Text generation / 273
F. Natural language processing systems / 281
1. Early natural language systems / 281
2. Wilks's machine translation system / 288
3. LUNAR / 292
4. SHRDLU / 295
5. MARGIE / 300
6. SAM and PAM / 306
7. LIFER / 316
V. Understanding Spoken Language / 323
A. Overview / 325
B. Systems architecture / 332
C. The ARPA SUR projects / 343
1. HEARSAY / 343
2. HARPY / 349
3. HWIM / 353
4. The SRI/SDC speech systems / 358
Bibliography for Volume I / 363
Name Index for Volume I / 391
Subject Index for Volume 1/397
CONTENTS OF VOLUME II
VI. Programming Languages for AI Research / 1
A. Overview / 3
B. LISP / 15
C. AI programming-language features / 30
1. Overview / 30
2. Data structures / 34
3. Control structures / 45
4. Pattern matching / 58
5. Programming environment / 65
D. Dependencies and assumptions / 72
VII. Applications-oriented AI Research: Science / 77
A. Overview / 79
B. TEIRESIAS / 87
C. Applications in chemistry / 102
1. Chemical analysis / 102
2. The DENDRAL programs / 106
a. Heuristic DENDRAL / 106
b. CONGEN and its extensions /111
c. Meta-DENDRAL / 116
3. CRYSALIS / 124
4. Applications in organic synthesis / 134
D. Other scientific applications / 143
1. MACSYMA / 143
2. The SRI Computer-based Consultant / 150
3. PROSPECTOR / 155
4. Artificial Intelligence in database management / 163
VIII. Applications-oriented AI Research: Medicine / 175
A. Overview / 177
B. Medical systems / 184
1. MYCIN / 184
2. CASNET / 193
3. INTERNIST / 197
4. Present Illness Program / 202
5. Digitalis Therapy Advisor / 206
6. IRIS / 212
7. EXPERT / 217
IX. Applications-oriented AI Research: Education / 223
A. Overview / 225
B. ICAI systems design / 229
C. Intelligent CAI systems / 236
1. SCHOLAR / 236
2. WHY / 242
3. SOPHIE / 247
4. WEST / 254
5. WUMPUS / 261
6. GUIDON / 267
7. BUGGY / 279
8. EXCHECK / 283
D. Other applications of AI to education / 291
X. Automatic Programming / 295
A. Overview / 297
B. Methods of program specification / 306
C. Basic approaches / 312
D. Automatic programming systems / 326
1. PSI and CHI / 326
2. SAFE / 336
3. The Programmer's Apprentice / 343
4. PECOS / 350
5. DEDALUS / 355
6. Protosystem I / 364
7. NLPQ / 370
8. LIBRA / 375
Bibliography for Volume II / 381
Indexes for Volume II / 403
CONTENTS OF VOLUME III
List of Contributors / xi
Preface / xiii
XI. Models of Cognition / 1
A. Overview / 3
B. General Problem Solver / 11
C. Opportunistic problem solving / 22
D. EPAM / 28
E. Semantic network models of memory / 36
1. Quillian's semantic memory system / 36
2. HAM / 42
3. ACT / 50
4. MEMOD / 56
F. Belief systems / 65
XII. Automatic Deduction / 75
A. Overview / 77
B. The resolution rule of inference / 86
C. Nonresolution theorem proving / 94
D. The Boyer-Moore theorem prover / 102
E. Nonmonotonic logics / 114
F. Logic programming / 120
XIII. Vision / 125
A. Overview / 127
B. Blocks-world understanding / 139
1. Roberts / 139
2. Guzman /143
3. Falk /147
4. Huffman-Clowes / 155
5. Waltz / 161
6. Shirai / 168
7. Mackworth / 173
8. Kanade / 183
C. Early processing of visual data / 195
1. Visual input / 195
2. Color / 203
3. Preprocessing / 206
4. Edge detection and line finding / 216
5. Region analysis / 225
6. Texture / 230
D. Representation of scene characteristics / 238
1. Intrinsic images / 238
2. Motion / 244
3. Stereo vision / 249
4. Range finders / 254
5. Shape-from methods / 260
6. Three-dimensional shape description and recognition / 268
E. Algorithms for vision / 279
1. Pyramids and quad trees / 279
2. Template matching / 283
3. Linguistic methods for computer vision / 287
4. Relaxation algorithms / 292
F. Vision systems / 301
1. Robotic vision / 301
2. Organization and control of vision systems / 306
3. ACRONYM / 313
XIV. Learning and Inductive Inference / 323
A. Overview / 325
B. Rote learning / 335
1. Issues / 335
2. Rote learning in Samuel's Checkers Player / 339
C. Learning by taking advice / 345
1. Issues / 345
2. Mostow's operationalizer / 350
D. Learning from examples / 360
1. Issues / 360
2. Learning in control and pattern recognition systems / 373
3. Learning single concepts / 383
a. Version space / 385
b. Data-driven rule-space operators / 401
c. Concept learning by generating and testing plausible hypotheses / 411
d. Schema instantiation / 416
4. Learning multiple concepts / 420
a. AQ11 / 423
b. Meta-DENDRAL / 428
c. AM / 438
5. Learning to perform multiple-step tasks / 452
a. Samuel's Checkers Player / 457
b. Waterman's Poker Player / 465
c. HACKER / 475
d. LEX / 484
e. Grammatical inference / 494
XV. Planning and Problem Solving / 513
A. Overview / 515
B. STRIPS and ABSTRIPS / 523
C. Nonhierarchical planning / 531
D. Hierarchical planners / 541
1. NOAH / 541
2. MOLGEN / 551
E. Refinement of skeletal plans / 557
Bibliography for Volume III / 563
Cumulative Indexes for Volumes I, II, and III / 587
CONTENTS OF VOLUME IV
XVI. Blackboard Systems / 1
H. Penny Nii
A. Overview / 3
B. Blackboard Model of Problem Solving / 4
1. The Blackboard Model / 4
2. The Blackboard Framework / 11
3. Perspectives / 16
4. Summary / 16
C. Evolution of Blackboard Architectures / 18
1. Prehistory / 18
2. The HEARSAY Project / 20
3. The HASP Project / 24
D. Blackboard Application Systems / 27
1. HEARSAY-II / 27
2. HASP/SIAP / 36
3. TRICERO / 50
4. PROTEAN / 56
5. Summary / 65
E. Summary: Elements of Blackboard Architecture / 67
1. Blackboard Systems and Task Characteristics / 68
2. "Problem Solving" Revisited: Search vs. Recognition / 70
3. Component Design / 74
XVII. Cooperative Distributed Problem Solving / 83
Edmund H. Durfee, Victor R. Lesser, Daniel D. Corkill
A. Overview / 85
B. An Example of CDPS / 95
C. Important CDPS Approaches and Empirical Investigations / 106
1. Negotiation / 107
2. Functionally Accurate Cooperation / 116
3. Organizational Structuring / 122
4. Multiagent Planning / 134
5. Sophisticated Local Control / 137
6. Formal Frameworks / 143
D. Conclusion / 146
XVIII. Fundamentals of Expert Systems / 149
Bruce G. Buchanan, Reid G. Smith
A. Overview / 151
B. Fundamental Principles / 160
1. Representation of Knowledge / 161
2. Reasoning Methods / 167
3. Knowledge Base Development / 173
4. Explanation / 174
5. System-building Tools/Shells / 175
6. Validation / 177
7. Reasons for Using the Methods of Expert Systems / 178
C. State of the Art / 181
1. Size of System / 181
2. Type of System / 183
3. Some Observed Limitations / 184
D. Design Principles and Summary / 189
1. Design Principles / 189
2. Summary / 191
XIX. Natural Language Understanding / 193
James Allen
A. Overview / 195
B. Unification Grammars / 198
C. Semantic Interpretation / 206
D. Semantic Interpretation Strategies / 213
E. Modeling Context / 223
F. Discourse Structure / 233
G. Conclusion / 238
XX. Knowledge-based Software Engineering / 241
Michael Lowry, Raul Duran
A. Overview / 243
B. Specification Acquisition / 253
1. Knowledge-based Specification Acquisition / 253
2. Specification Languages / 255
3. Specification Acquisition Methodologies / 258
4. Specification Validation / 261
5. Specification Maintenance / 263
6. Recovering Specifications from Code / 265
C. Program Synthesis / 268
1. Historical Perspective / 269
2. Transformational Approach / 272
3. Deductive Approach / 274
4. Basic Rules / 275
5. Large-grained Rules / 281
6. Reusing Derivations / 282
7. Basic Search Techniques / 284
8. Knowledge-intensive Search Techniques / 286
D. Systems for Specification Acquisition / 292
1. IDeA / 292
2. Explainable Expert Systems / 294
3. DRACO / 295
4. The Requirements Apprentice / 296
5. KATE / 298
6. Ozym / 299
7. Watson / 301
E. Program Synthesis Systems / 303
1. CIP / 303
2. Designer / 304
3. KIDS / 307
4. MEDUSA / 310
5. KBEmacs / 311
6. REFINE / 313
7. SETL / 315
8. STRATA / 316
9. ELF / 318
10. PHiNix / 320
F. Further Readings / 322
XXI. Qualitative Physics / 323
Yumi Iwasaki
A. Overview / 325
B. Qualitative Calculus / 339
C. Reasoning About Behavior Using Qualitative Calculus / 350
1. Qualitative Behavior and Qualitative States / 350
2. State Transitions / 351
3. Difficulties in Qualitative Prediction / 357
D. ENVISION / 362
1. Device Model / 362
2. Predicting Behavior / 366
3. Conclusion / 368
E. Qualitative Process Theory / 371
1. Representation of Objects / 372
2. Process Representation / 373
3. Predicting Behavior / 376
4. Conclusion / 380
F. QSIM / 382
G. Causal Ordering / 392
H. Causal Action/Event-based Approaches / 403
1. Commonsense Algorithm / 403
2. Functional Representation of Devices / 406
3. Consolidation / 409
4. Conclusion / 411
XXII. Knowledge-based Simulation / 415
Alfred Round
A. Overview / 417
B. The Evolution of Knowledge-based Simulation / 419
1. An Overview of Simulation / 419
2. The Limitations of Numerical Simulation / 424
3. Object-oriented Language for Simulation / 425
C. Applications of Knowledge-based Simulation / 437
1. The Design of Flexible Manufacturing Systems / 437
2. Planning Therapies for Cancer Treatment / 441
3. Evaluating Business Proposals / 444
4. Solving Problems in Molecular Genetics / 448
D. The Design of Knowledge-based Simulation Systems / 452
1. Sequential Integrated Systems / 452
2. Parallel Integrated Systems / 455
3. Intelligent Front Ends for Building Numerical Simulation / 459
4. Rule-driven Simulation / 462
E. Qualitative Aspects of Knowledge-based Simulation / 464
1. Simplification of Processes / 464
2. Aggregation of Processes / 467
3. Multiple Levels of Abstraction / 470
4. Multiple Levels of Precision / 476
F. Real- World Applications of Knowledge-based Simulation / 483
1. COMAX: Knowledge-based Simulation for Cotton Crop Management / 483
2. SimKit: An Integrated, Knowledge-based Environment for Simulation / 487
3. ABLE: Knowledge-based Control for Particle Accelerators / 493
4. Forecast Pro: Intelligent Prediction of Business Trends / 500
G. Issues in the Development and Use of Knowledge-based Simulation / 504
1. Simulation, Inferencing, and Time / 504
2. The Development of Knowledge-based Simulation Applications / 510
3. The Validation of Knowledge-based Simulation Applications / 513
H. Conclusion / 518
XXIII. Computer Vision Update / 519
Robert M. Haralick, Alan K. Mackworth, Steven L. Tanimoto
A. Overview / 521
B. Low-level Vision / 523
1. Segmentation Techniques / 523
2. Edges / 534
3. Stereo / 536
4. Mathematical Morphology for Image Analysis / 539
C. Computational Vision Advances / 547
1. Shape Representation and Analysis / 547
2. Criteria for Shape Representation / 547
3. Object Recognition / 558
4. Constraint Satisfaction / 560
D. Vision Architecture / 565
Bibliography / 583
Name Index / 623
Subject Index / 643
Электронный (англоязычный) вариант справочника доступен для скачивания:
Avron Barr. Handbook of Artificial Intelligence, [Volume 1, 1st ed.] 0865760055, 0865760896, William Kaufmann Inc. / HeurisTech Press, 1981, 421 p., English, 3 Mb, djvu [1] [2] [3] [4] [5] [6] [7]
Avron Barr, Edward A. Feigenbaum. Handbook of Artificial Intelligence, [volume 2, 1st ed.] 0865760063 William Kaufmann Inc. / HeurisTech Press, 1982, 440 p., English, 4 Mb, djvu [1] [2] [3] [4] [5] [6] [7]
Paul R. Cohen, Edward A. Feigenbaum. Handbook of Artificial Intelligence, [volume 3, 1st ed.] 0865760071, Addison-Wesley Pub Co (Trd), 1982, 656 p., English, 6 Mb, djvu [1] [2] [3] [4] [5] [6] [7]
Handbook of artificial intelligence [volume 4, 1989] PDF (41.7 M), DjVu (17.7 M)
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