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Emphasizing a hands-on approach for building knowledge-based systems, this guide covers both the theory and the practical applications of knowledge-based systems. KEY TOPICS: It presents the theoretical foundations of knowledge-based systems as a branch of artificial intelligence, and then concentrates on practical aspects in the development of a knowledge based system. For professionals working in the fields of knowledge-based engineering and artificial intelligence.

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CONTENTS
1 INTRODUCTION TO KNOWLEDGE-BASED SYSTEMS = 1
 1.1 Introduction = 1
 1.2 Algorithmic Methods in Computing = 3
 1.3 Search as the Foundation of Artificial Intelligence = 3
  1.3.1 Searching a Problem Space =  9
  1.3.2 Depth-first Search = 10
  1.3.3 Breadth-first Search = 10
  1.3.4 Beam Search and the Use of Knowledge =  11
  1.3.5 Hill-climbing Search = 11
  1.3.6 Branch and Bound Search = 12
  1.3.7 Best-first Search = 14
  1.3.8 A* Search = 15
 1.4 Expertise and Heuritic Knowledge = 19
 1.5 Knowledge-based Systems-A Definition = 21
 1.6 Knowledge-based Systems and the Various Types of Expertise = 24
  1.6.1 Associational Knowledge = 24
  1.6.2 Motor Skills = 25
  1.6.3 Theoretical(Deep) Knowledge = 25
 1.7 Features of Knowledge-based Systems = 26
  1.7.1 Advantages = 26
  1.7.2 Disadvantages = 27
 1.8 GenAID-A Case Study = 28
 1.9 Development of Knowledge-based Systems = 30
 1.10 Chapter Review = 31
 1.11 Problems = 32
2 KNOWLEDGE-BASED SYSTEMS STRUCTURE = 34
 2.1 Introduction = 34
 2.2 System Components-End-User's View = 35
  2.2.1 The Intelligent Program = 35
  2.2.2 The User Interface = 35
  2.2.3 The Problem-specific Database = 36
 2.3 System Components-Knowledge Engineer's View = 37
  2.3.1 Intelligent Program = 37
   2.3.1.1 The Knowledge base = 37
   2.3.1.2 The inference engine = 38
  2.3.2 The Development Shell = 40
   2.3.2.1 Knowledge acquisition tool = 40
   2.3.2.2 Test case database = 40
   2.3.2.3 Developer's interface = 41
 2.4 System Components-Tool Builder's View = 41
 2.5 Knowledge-based Tools = 43
  2.5.1 Shells = 43
  2.5.2 Developing a System from Scratch = 44
 2.6 Chapter Review = 45
 2.7 Problems = 46
3 LOGIC AND AUTOMATED REASONING = 47
 3.1 Introduction = 47
 3.2 Propositional Logic = 48
 3.3 Predicate Logic-A Means of Representing Knowledge = 51
  3.3.1 Predicates and Terms = 52
  3.3.2 Variables and Quantifiers = 53
  3.3.3 Unification = 55
  3.3.4 Converting English Statements into wff's = 57
 3.4 Logical Inferences and Automated Reasoning-Manipulating the Knowledge = 58
  3.4.1 Deduction = 60
  3.4.2 Abduction = 61
  3.4.3 Indudction = 62
  3.4.4 Automated Theorem Proving and Resolution = 63
  3.4.5 Monotonic Verses Nonmonotonic Reasoning = 66
 3.5 PROLOG = 69
  3.5.1 A Basic Introduction = 69
  3.5.2 Facts = 70
  3.5.3 Rules = 71
  3.5.4 Backtracking and the Inference Process in PROLOG = 72
  3.5.5 The CUT = 78
 3.6 Advantages and Disadvantages of Predicate Logic as a Basis for a    Knowledge-based System = 81
 3.7 Chapter Review = 82
 3.8 Problems = 83
4 INTRODUCTION TO RULE-BASED REASONING = 86
 4.1 Introduction = 86
 4.2 What are Rules? = 87
 4.3 Rule-based Inference = 88
 4.4 The Reasoning Process = 90
  4.4.1 Forward Reasoning = 94
  4.4.2 Backward Reasoning = 96
 4.5 Rule-based Architectures = 99
  4.5.1 Inference Networks = 99
  4.5.2 Pattern-matching Systems = 101
  4.5.3 Evaluation of the Architectures = 103
 4.6 Desadvantages of Rule-based Systems = 103
  4.6.1 Inficite Chaining = 104
  4.6.2 Addition of New, Contradictory Knowledge = 104
  4.6.3 Modifications to Existing Rules = 106
  4.6.4 Additional Disadvantages = 107
 4.7 Advantages of Rule-based Systems = 108
 4.8 Chapter Review = 108
 4.9 Problems = 110
5 DETAILS OF RULE-BASED REASONING = 112
 5.1 Introduction = 112
 5.2 Forward Reasoning = 112
  5.2.1 Example 1 of Forward Reasoning = 113
  5.2.3 Example 2 of Forward Reasoning = 119
  5.2.3 The Rete Algorithm = 127
  5.2.4 Conflict Resolution Schemes = 132
  5.2.5 Coding Forward Reanoning(optional) = 134
 5.3 Backward or Goal-directed Reasoning = 138
  5.3.1 Example 1 of Backward Reasoning = 138
  5.3.2 Example 2 of Backward Reasoning = 142
  5.3.3 Coding a Backward-Reasoning System(optional) = 151
 5.4 Chapter Review = 155
 5.5 Problems = 156
6 ASSOCIATIVE NETWORKS, FRAMES, AND OBJECTS = 159
 6.1 Introduction = 159
 6.2 Associative(Semantic) Networks = 159
  6.2.1 General Introduction = 160
  6.2.2 Associative Network Example = 167
  6.2.3 Coding Associative Networks(optional) = 170
  6.2.4 Advantages and Disadvantages of Associative Networks = 173
 6.3 Frames =  174
  6.3.1 General Introduction = 174
  6.3.2 A More Detailed Example = 177
  6.3.3 Implemantation of Frames(optional) = 189
  6.3.4 Frames in Knowledge-based Systems = 195
  6.3.5 Advantages and Disadvantages of Frames = 196
 6.4 Objects = 197
  6.4.1 General Introduction = 197
  6.4.2 Historical Perspective = 199
  6.4.3 Objec-oriented Extensions to LISP = 199
   6.4.3.1 General Features = 200
  6.4.4 Advantages and Disadvantages = 203
 6.5 Chapter Review = 204
 6.6 Problems = 205
7 BLACKBOARD ARCHITECTURES = 207
 7.1 Introduction = 207
 7.2 The Blackboard Framework = 208
  7.2.1 The Knowledge Sources = 210
  7.2.2 The Blackboard = 211
  7.2.3 The Control = 211
  7.2.4 Execution of Blackboard System = 213
 7.3 Historical Perspective = 213
 7.4 Hearsay = 215
 7.5 Crysalis = 220
 7.6 AGE = 224
 7.7 Blackboard Development Environments = 226
 7.8 Advantages and Disadvantages = 228
 7.9 Chapter Review = 230
 7.10 Problems = 230
8 UNCERTAINTY MANAGEMENT = 232
 8.1  Introduction = 232
 8.2 Bayesian Approaches = 233
  8.2.1 Background = 233
  8.2.2 Bayes' Rule and Knowledge-based Systems = 235
  8.2.3 Propagation of Belief = 237
  8.2.4 Advantages and Disadvantages of Bayesian Methods = 238
 8.3 Certaintly Factors = 239
  8.3.1 Certainty Factor (CF) Formalism = 240
  8.3.2 Propagation of Certainty Factors = 241
  8.3.3 Dealing with Uncertain Evidence = 241
  8.3.4 Certainty Factor Example = 242
  8.3.5 Advantages and Disadvantages of Certainty Factors = 246
 8.4 Dempster-Shafer Theory of Evidence = 247
  8.4.1 Definition of Terms = 247
  8.4.2 Example = 249
  8.4.3 Evaluation = 253
 8.5 Fuzzy Sets and Fuzzy Logics = 254
  8.5.1 Definition of Terms = 255
  8.5.2 Example = 257
  8.5.3 Evaluation = 259
 8.6 Chapter Review = 260
 8.7 Problems = 261
9 ADVANCED REASONING TECHNIQUES = 263
 9.1 Introduction = 263
 9.2 Model-based Reasoning in Diagnostic Applications = 265
  9.2.1 Description of a Model-Based Reasoning System = 267
  9.2.2 Example of Model-based Reasoning = 270
  9.2.3 Advantages and Disadvantages of Model-based Reasoning = 272
 9.3 Qualitative Reasoning = 273
  9.3.1 QSIM-A Qualitative Reasoning Language = 274
  9.3.1 A Qualitative Simulation Example Using QSIM = 276
 9.4 Case-based Reasoning = 279
  9.4.1 Example of Case-based Reasoning = 281
  9.4.2 Advantages and Disadvantages of Case-based Reasoning = 283
 9.5 Temporal Reasoning = 284
  9.5.1 Intervals and Points of Time = 288
  9.5.2 The Time Unit System = 288
  9.5.3 Temporal Reasoning Example = 289
 9.6 Artificial Neural Networks = 292
 9.7 Chapter Review = 294
 9.7 Problems = 295
10 THE KNOWLEDGE-BASED SYSTEM LIFECYCLE = 296
 10.1 Introduction = 296
 10.2 The Lifecycle of Conventional Software = 296
 10.3 Differences from Knowledge-based Systems = 300
 10.4 The Knowledge-based System Lifecycle Stages = 304
 10.5 Chapter Review = 307
11 FEASIBILITY ANALYSIS = 308
 11.1 Introduction = 308
 11.2 Suitability of the Application = 309
  11.2.1 Does a Problem Really Exist? = 309
  11.2.2 Is a Knowledge-based Technique Suited? = 310
  11.2.3 Is a Knowledge-based Approach Justified? = 312
 11.3 Availability of Resources = 313
  11.3.1 Is There Management Support for the Project? = 313
  11.3.2 Is There Support on the part of the Expert? =  314
  11.3.3 Is the Expert Competent? = 314
  11.3.4 Is the Expert Articulate? = 315
  11.3.5 Is the Expert in Physical Proximity? = 315
 11.4 Sample Application = 316
 11.5 Chapter Review = 319
 11.6 Problems = 320
12 REQUIREMENTS SPECIFICATION AND DESIGN = 324
 12.1 Introduction = 324
 12.2 Prquirements Specification = 325
 12.3 Preliminary Design = 328
  12.3.1 Selection of a Knowledege Representation Paradigm = 328
  12.3.2 Reasoning Method Selection = 329
  12.3.3 Tool Selection = 330
   12.3.3.1 Custom or commercial shell? = 330
   12.3.3.2 Criteria for selecting commercially available shells = 331
  12.3.4 Selection of Human Resources = 335
   12.3.4.1 Choosing the right knowledge engineer = 335
   12.3.4.2 Choosing the right team leader = 337
   12.3.4.3 Choosing the right expert = 338
  12.3.5 Development Team Requirements = 339
   12.3.5.1 Small knowledge-based systems = 339
   12.3.5.2 Medium systems = 340
   12.3.5.3 Large systems = 342
 12.4 Initial Prototype(IP) = 343
 12.5 Detailed Design = 344
 12.6 Chapter Review = 346
 12.7 Problems = 346
13 KNOWLEDGE ACQUISITION AND SYSTEM IMPLEMENTATION = 348
 13.1 Introduction = 348
 13.2 Knowledge Aiquisition = 348
 13.3 The Basic Unstructured One-on-one Interview = 349
  13.3.1 Kickoff Interview = 350
  13.3.2 Knowledge Elicitation Sessions = 351
   13.3.2.1 General Knowledge-gathering sessions = 352
   13.3.2.2 Specific problem-solving, knowledge-gathering sessions = 353
  13.3.3. Knowledge Organization = 354
  13.3.4. Knowledge Documentation = 356
 13.4 Knowledge Elicitation Techniques = 360
  13.4.1 Observational Techniques = 361
   13.4.1.1 Quiet on-site observation = 361
   13.4.1.2 On-site obsevation with discussion = 362
   13.4.1.3 Exercising the expert = 362
   13.4.1.4 Problem description and analysis = 363
  13.4.2 Intuitive Techniques = 364
 13.5 Chapter Review = 364
 13.6 Problems = 365
14 PRACTICAL CONSIDERATIONS IN KNOWLEDGE ACQUISITION = 367
 14.1 Introduction = 367
 14.2 Team Interviewing = 367
  14.2.1 One-on-many Interviews = 368
  14.2.2 Many-on-one Interviews = 369
  14.2.3 Many-on-many Interviews = 370
 14.3 Planning Interviews = 370
  14.3.1 Interview Location = 370
  14.3.2 Interview Schedules = 371
  14.3.3 Preparation of Interview = 371
   14.3.3.1 Review of prior work = 372
   14.3.3.2 Setting session objectives =  372
 14.4 Conducting the Interview = 373
  14.4.1 Interpersonal Communications = 374
  14.4.2 Recording the Information Gained in Meeting =   375
 14.5 Handling Problem Experts = 376
  14.5.1 The Wimp Expert =  376
  14.5.2 The Cynical Expert = 377
  14.5.3 The High Priest of the Domain Expert = 377
  14.5.4 The Paternalistic Expert = 378
  14.5.5 The Uncommunicative Expert = 378
  14.5.6 The Uncaring Expert = 378
  14.5.7 The Pseudo-AI-literate Expert = 379
 14.6 Chapter Review = 379
15 ALTERNATIVE KNOWLEDGE ACQUISITION MEANS = 381
 15.1 The Knowledge Acquisition Bottleneck = 381
 15.2 Facilitating the Knowledge Acquisition Process = 382
 15.3 Machine Learning = 387
  15.3.1 Inductive Reasoning-Learning from Examples = 388
  15.3.2 Knowledge Acquisition through Inductive Tools = 396
 15.4 Automated Knowledge Extraction from Databases = 397
 15.5 Chapter Review = 405
 15.6 Problems = 406
16 VERIFICATION AND VALIDATION = 408
 16.1 Introduction = 408
 16.2 A Comparison of the V&V of Knowledge-based Systems and Conventional Software = 409
 16.3 Verification = 411
  16.3.1 Specification Compliance = 412
  16.3.2 Developer-induced Errors = 412
   16.3.2.1 Redundant rules = 413
   16.3.2.2 Conflicting rules = 414
   16.3.2.3 Subsumed rules = 414
   16.3.2.4 Circular rules = 414
   16.3.2.5 Unnecessary IF conditions = 415
   16.3.2.6 Dead-end rules = 415
   16.3.2.7 Missing rules = 416
   16.3.2.8 Unreachable rules = 416
  16.3.3 When Semantic Errors Are Not Errors = 416
  16.3.4 Verification Tools = 417
 16.4 Validation = 418
  16.4.1 Significant Issues in Validation = 418
   16.4.1.1 What to validate = 419
   16.4.1.2 Validation methodology = 419
   16.4.1.3 Validation criteria = 422
   16.4.1.4 When is validation appropriate? = 423
  16.4.2 Errors = 424
 16.5 Case Studies-MYCIN and R1 = 425
 16.6 A Practical Approach to V&V = 428
 16.7 Recommended Procedure for V&V = 430
 16.8 Chapter Review = 433
17 LEGAL ISSUES IN KNOWLEDGE-BASED SYSTEMS = 435
 17.1 Introduction = 435
 17.2 The Law of Torts = 436
 17.3 Are Computer Programs Products or Services? = 437
 17.4 What About Knowledge-based Systems? = 438
 17.5 Software Protection = 440
  17.5.1 Copyright Protection = 441
  17.5.2 Patent Protection = 441
  17.5.3 Trade Secret Protection = 442
 17.6 Chapter Review = 443
APPENDIX A THE CLIPS SYSTEM = 445
 A.1 Introduction = 445
 A.2 The CLIPS System = 445
 A.3 Facts = 446
  A.3.1 Assertion of Facts = 446
  A.3.2 Retracting Facts = 447
  A.3.3 Initial Facts = 448
 A.4 Rules = 449
 A.5 Variables, Operators, and User-defined Functions = 451
  A.5.1 Variables = 451
  A.5.2 Special Sysmbols in Pattern = 453
   A.5.2.1 The wildcard = 453
   A.5.2.2 Field constraints = 454
   A.5.2.3 Mathematical operators = 455
   A.5.2.4 The test feature = 456
   A.5.2.5 Pattern connectives = 456
   A.5.3 User-defined Functions = 458
 A.6 Input/Output in CLIPS = 458
  A.6.1 The Fprintout Function = 458
  A.6.2 File I/O = 459
  A.6.3 Terminal Inputs = 459
  A.6.4 The MicroEMACS Editor = 460
 A.7 The CLIPS User Inerface = 460
 A.8 Some Final Notes on CLIPS = 468
APPENDIX B THE PERSONAL CONSULTANT PLUS SHELL SYSTEM = 472
 B.1 Introduction = 472
 B.2 Parameters = 473
  B.2.1 Parameter Properties = 474
  B.2.2 Required Parameters = 474
   B.2.2.1 The YES/NO parameter = 474
   B.2.2.2 The SINGLEVALUED parameter =  474
   B.2.2.3 The MULTIVALUED parameter = 475
   B.2.2.4 The ASK-ALL type = 475
   B.2.2.5 Parameter Example = 475
  B.2.3 Internal Parameter Properties = 477
  B.2.4 Optional parameter Properties = 477
 B.3 Rules = 478
  B.3.1 The Abbreviated Rule Language = 479
   B.3.1.1 Arithmetic functions in ARL = 480
   B.3.1.2 Predicate functions = 480
   B.3.1.3 Text and graphics ARL functions = 483
   B.3.1.4 The conclusion ARL functions = 485
   B.3.1.5 The auxiliary ARL funcions = 485
  B.3.2 Rule Properties = 485
  B.3.3 Meta-rules = 486
 B.4 The Knowledge Base Properties = 486
  B.4.1 The GOALS Property = 487
  B.4.2 The INITIALDATA Property = 487
  B.4.3 The DOMAIN Property = 487
  B.4.4 The TITLE Property = 488
  B.4.5 The PROMPTEVER and GPROMPTEVER Properties = 488
  B.4.6 The DISPLAYRESULTS Property = 488
 B.5 Certainty Factor Propagation = 488
  B.5.1 Certainty Factors = 489
  B.5.2 Belief Propagation = 490
 B.6 Interfacing with PC Plus = 491
  B.6.1 The Develper's Interface = 492
   B.6.1.1 The activities screen = 493
   B.6.1.2 The knowledge base screen = 494
   B.6.1.3 The properties screen = 495
   B.6.1.4 The Parameters screen = 496
   B.6.1.5 The rules screen = 498
  B.6.2 Additional Features of PC Plus = 499
   B.6.2.1 The HOW command = 500
   B.6.2.2 The WHY command = 500
   B.6.2.3 The REVIEW command = 500
 B.7 Final Notes About PC Plus = 500
REFERENCES = 501
INDEX = 515