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      Knowledge engineering shells : systems and techniques

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      https://www.riss.kr/link?id=M296608

      • 저자
      • 발행사항

        Singapore ; River Edge, NJ : World Scientific, c1993

      • 발행연도

        1993

      • 작성언어

        영어

      • 주제어
      • DDC

        006.3/3 판사항(20)

      • ISBN

        9810210566

      • 자료형태

        단행본(다권본)

      • 발행국(도시)

        싱가포르

      • 서명/저자사항

        Knowledge engineering shells : systems and techniques / edited by Nikolaos G. Bourbakis.

      • 형태사항

        xxiv, 539 p. : ill. ; 23 cm.

      • 총서사항

        Advanced series on artificial intelligence ; vol. 2

      • 일반주기명

        Includes bibliographical references.

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      목차 (Table of Contents)

      • CONTENTS
      • PROGOGUE = ⅴ
      • ANLINTRODUCTION TO KNOWLEDGE ENGINEERING / N. Bourbakis = ⅶ
      • CONTRIBUTORS = xi
      • SECTION 1 : KNOWLEDGE REPRESENTATION AND MANIPULATION
      • CONTENTS
      • PROGOGUE = ⅴ
      • ANLINTRODUCTION TO KNOWLEDGE ENGINEERING / N. Bourbakis = ⅶ
      • CONTRIBUTORS = xi
      • SECTION 1 : KNOWLEDGE REPRESENTATION AND MANIPULATION
      • Chapter 1 : KNOWLEDGE REPRESENTATION USING SEMANTICS OF SEMANTIC NETWORKS / A. V. Hudli
      • 1. Introduction = 3
      • 1.1. NVL = 5
      • 2. Syntax of NVL = 6
      • 2.1. The Basis for NVL Syntax = 6
      • 2.2. Symbols Used in NVL = 11
      • 2.3. Formation Rules = 11
      • 2.4. Axiom schemata and Rules of Inference = 12
      • 3. Semantics of NVL Formulas = 15
      • 4. Unification of NVL Lists = 17
      • 4.1 Parallel Unification = 18
      • 4.2 Answer Substitutions = 19
      • 5. The NVL Interpreter = 20
      • 5.1. Completion of an NVL Program = 20
      • 5.2. Simple Inferences = 20
      • 5.3. V-Resolution = 21
      • 6. Comparing NVL with Predicate Logic = 22
      • 6.1. The Relationship between NVL and predicate Logic = 22
      • 6.2. Efficiency of NVL = 24
      • 7. Increasing the Expressive Power of NVL = 25
      • 7.1. Adding Negation = 25
      • 7.2. Limitor Lists with Constraints = 26
      • 8. Conclusions = 27
      • References = 27
      • Chapter 2 : AN ONTOLOGICAL APPROACH TO KNOWLEDGE ACQUISITION SCHEMES / I. Monarch and S. Nirenburg
      • 1. Requirements for Very Large Dynamic Knowledge Bases = 30
      • 2. Categorial and Experienctial Knowledge = 31
      • 2.1. Semantic Memory = 31
      • 2.2. World Models = 34
      • 2.3. Episodic Memory = 36
      • 3. Maintenance of VLKBs = 37
      • 3.1. Views at Kowledge Representation = 37
      • 3.2. The Architecture of a VLKB = 42
      • 4. Ac Acquisition of VLKBs = 44
      • 4.1. Acquisition Systems and Methodologies = 44
      • 4.2. Extensions to ONTOS : Incremental Automation = 45
      • References = 49
      • Chapter 3 : PRODUCTION RULES AND SYSTEMS : A TOP-DOWN CONSTRUCTION OF BOTTOM-UP INFERENCE / M. Perlin
      • 1. Introductioon = 57
      • 2. A Concrete Problem = 58
      • 3. A Backward Chanining Solution = 60
      • 4. A Forward Chaining Solution = 62
      • 4.1. Acquiring Production Rules from the Solution Trace = 62
      • 4.2. Inductive Learning = 66
      • 5. Production Systems= 66
      • 6. Rule Interpreters = 68
      • 7. The RETE Match Algorithm = 69
      • 7.1. Set Filtering = 69
      • 7.2. Recursive Set Filtering = 70
      • 7.3. Evaluating the Recursion = 72
      • 7.4. Network Programs = 73
      • 7.5. Incremental Match= 75
      • 7.6. Merging Tests = 76
      • 8. Extensions to Basic RETE = 77
      • 8.1. Control by Data-Driven Agenda = 78
      • 8.2. Compiling Tests = 78
      • 8.3. Absence Tests = 79
      • 8.4. Equality Testing Using Binding Values = 80
      • 8.5. Active Tuple Memory = 81
      • 9. Conclusions = 82
      • References = 83
      • Chapter 4: TOWARDS PARALLEL KNOWLEDGE PROCESSING / J.C. Yan
      • 1. Introduction = 87
      • 1.1. Knowledge Processing Paradigms = 88
      • 1.2. Parallel Processing = 90
      • 1.3. Chapter Outline = 91
      • 2. Sequential Knowledge Processing = 92
      • 2.1. Software and Hardware Requirements of Knowledge Processing = 92
      • 2.2. LISP Machines=92
      • 2.3. PROLOG Machines = 94
      • 2.4. The RETE Algorithm= 94
      • 2.5. Summary : Sequential Knowledge Processing is Still Too Slow = 95
      • 3. Multiprocessors for Knowledge Processing = 96
      • 3.1. DADO = 96
      • 3.2. NETL = 97
      • 3.3. Connection Machine = 97
      • 3.4. Parallel Inference Machine = 98
      • 3.5. Summary : Parallel Hardware for Knowledge Processing = 99
      • 4. Parallel Languages for Knowledge Processing = 99
      • 4.1. parallel LISPs = 99
      • 4.2. parallel PROLOGs = 101
      • 4.3. parallel Object-Oriented Languages = 103
      • 4.4. summary : Parallel Languages for Knowledge Processing = 104
      • 5. Mapping Knowledge Processing Systems to Multicomputers = 104
      • 5.1. Mapping Production Systems onto DADO = 105
      • 5.2. Mapping RETE Networks onto Multicomputers = 106
      • 5.3. Distributing Rules on Multicomputers = 107
      • 5.4. summary : Mapping Expert Systems to Multiprocessors = 108
      • 6. Measuring parallelism in Knowledge Processing Systems = 108
      • 6.1. Parallelism in Production Systems and Flat Concurrent PROLOG Systems = 109
      • 6.2. Obtaining Speed-up via Parallel Processing is Difficult = 110
      • 7. Parallel Processing for Knowledge Processing Systems = 111
      • 7.1. Speed-up and Parallel Processing = 112
      • 7.2. Conclusions = 113
      • References = 115
      • SECTION 2 : EXPERT SYSTEMS
      • Chapter 5 : DEVELOPMENT OF EXPERT SYSTEM SHELLS / J.J.P.Tsai, S.J.H. Yang, N. Waheed and t. Moher
      • 1. Introduction = 123
      • 2. Knowledge Representation = 124
      • 2.1. Rules = 124
      • 2.2. Semantic Nets = 125
      • 2.3. Frames = 126
      • 2.4. Objects = 126
      • 3. Inference and Control = 126
      • 3.1. Rule Based Control = 127
      • 3.2. vp rocedural Control = 127
      • 3.3. Object Oriented Control = 128
      • 3.4. Access Oriented Control = 128
      • 4. Development Environment = 128
      • 5. Expert System Shell= 129
      • 5.1. Knowledge Engineering Languages = 129
      • 5.2. Skeletal Shells = 129
      • 5.3. General Purpose Shells = 130
      • 5.4. Knowledge Acquisition Tolls = 132
      • 6. Trends of Current Expert System Shells = 132
      • 7. Description of Some expert System Shells = 133
      • 8. List of Commercial Expert System Shells = 141
      • References = 150
      • Chapter 6 : DESIGNING BETTER METHODOLOGIES FOR EXPERT SYSTEM DEVELOPMENT / W.T.Tsai and I.A. Zualkernan
      • 1. Introduction = 153
      • 2. Expert System Development = 154
      • 3. Essence and Accidental Problems in Expert System Development = 157
      • 3.1. Extreme Wicked Problem and Exploratory Prototyping = 157
      • 3.2. ES Development is Large Scale = 159
      • 4. SE Solutions to Accidental Problems in ES Development = 160
      • 4.1. Logical Modularity of the Process = 160
      • 4.2. Functional Modularity of the Process = 161
      • 4.3. Product Modularity = 163
      • 4.4. Logical Modularity of Validation and Verification = 163
      • 5. A New Paradigm for Expert System Development = 166
      • 6. Example = 171
      • 6.1. Problem Specification = 171
      • 6.2. Solution Specification = 172
      • 6.3. Design Specification = 174
      • 6.4. Implementation = 174
      • 6.5. Verification and validation = 176
      • 7. Conclusions = 178
      • Reference = 179
      • Appendix = 186
      • Chapter 7 : KNOWBEL : NEW TOOLS FOR EXPERT SYSTEM DEVELOPMENT / H. Wang, J. Mylopoulos, A. Kushniruk, B. Kramer and M. Stanley
      • 1. Introduction = 189
      • 2. Background = 189
      • 2.1. Classification of Expert System Building Tools = 190
      • 2.2. Knowledge Representation in Commercial Expert System Tools = 192
      • 2.3. Experimental Knowledge Representation Systems = 195
      • 3. Development of KNOWBEL = 197
      • 3.1. System Architecture = 197
      • 3.2. The Telos Level = 198
      • 3.3. The MRS Level = 204
      • 3.4. The Temporal Reasoner = 207
      • 3.5. Reimplementation of KNOWBEl = 210
      • 3.6. AP Hypertext User Interface = 212
      • 4. Aplications = 218
      • 4.1. Expert Systems for Prcess Control : The APACS Project = 218
      • 4.2. Intelligent User Interfaces = 223
      • 5. Conclusions = 225
      • References = 227
      • Chapter 8 : COOP : A SELF-ASSESSMENT BASED APPROACH TO COOPERATING EXPERT SYSTEMS / S. Shekhar and C.V. Ramamoorthy
      • 1. Introduction = 231
      • 2. Model of Cooperation = 233
      • 3. Environment to Support C.E.S. Applications = 234
      • 4. Annotated PROLOG = 236
      • 4.1. Semantics of Annotaetd Prolog Programs = 238
      • 4.2. Semantics of APL = 239
      • 4.3. Proof Procedure for APL = 240
      • 4.4. Implementation = 241
      • 4.5. Sensitivity Analysis = 242
      • 5. Yellow Pages : Locating Relevant Expertise = 243
      • 5.1. Organizing the Knowledge = 244
      • 5.2. Implementation = 245
      • 6. How to Cooperate : Policies and Mechanisms = 248
      • 6.1. Cooperation PoliCies= 249
      • 6.2. Cooperation Mechanisms = 249
      • 7. Network Event Manager = 251
      • 8. Worm Mechanism = 252
      • 8.1. Design = 253
      • 8.2. Implementation = 254
      • 9. Status and Future Work = 255
      • References = 256
      • Chapter 9 : EXPERIENCES MADE USING THE EXPERT SYSTEM SHELL G2 / S. Nilsen
      • 1. Introduction = 259
      • 2. The General Need for Expert System Technology = 260
      • 3. Graphical Data Structures = 262
      • 4. The SAS-Ⅱ System = 262
      • 5. Requirements to the SAS-ⅡSystem = 264
      • 6. Basic G2 = 267
      • 7. Experiences Applying G2 for the SAS-ⅡProject = 268
      • 7.1. The Applicability of the G2 Tool = 268
      • 7.2. Structuring and Accessing the Knowledge Base = 271
      • 7.3. Getting Data Into and Out from SAS-Ⅱ = 273
      • 7.4. Time Efficiency and Stability = 273
      • 7.5. Adaptability to Future Work = 274
      • 8. Conclusion = 274
      • References = 275
      • SECTION 3 : KNOWLEDGE AND INFORMATION BASED SYSTEMS
      • Chapter 10 : KEDE - A HYBRID KNOWLEDGE ENGINEERING DEVELOPMENT ENVIRONMENT / Z. Zheng and W. Li
      • 1. Introduction = 279
      • 1.1. Spin-off Tools = 282
      • 1.2. Purpose Tools = 283
      • 1.3. Knowledge Engineering Languages = 284
      • 1.4. Hybrid Toolkits = 286
      • 2. Knowledge Representation = 289
      • 2.1. Frames = 289
      • 2.2. semantic Nets = 300
      • 2.3. Logic = 301
      • 2.4. Procedural Knowledge = 304
      • 2.5. Object Oriented Knowledge Representation = 304
      • 3. Relations = 305
      • 3.1. System-Defined Relations and Inheritance = 305
      • 3.2. User-Defined Relations = 307
      • 4. Data Oriented Programming = 308
      • 5. Object-Oriented Programming = 311
      • 5.1. The Classes and Instances = 311
      • 5.2. The Methods = 311
      • 5.3. Message Sending = 312
      • 6. Logic-Oriented Programing = 314
      • 6.1. Backward Chaining = 314
      • 6.2. Forward Chaining = 315
      • 6.3. Combining Forward and Backward Chaining = 316
      • Conclusions = 316
      • References = 317
      • Chapter 11 : KNOWLEDGE AS A KEY COMPONENT IN INTELLIGENT INFORMATION SYSTEMS / M. P. Papazoglou
      • 1. Introduction = 321
      • 2. Active Information Systems = 323
      • 2.1. The AIS Substrates = 323
      • 2.2. Knowledge Representation = 324
      • 2.3. Taxonomy of Knowledge = 327
      • 3. Organizational Knowledge = 327
      • 3.1 Intensional and Extensional Knowledge = 328
      • 4. Organizational Knowledge Querying = 329
      • 4.1. Querying the Factual Level = 329
      • 4.2. Querying the Conceptual Level = 330
      • 5. Concluding Remarks and Further Research = 338
      • References = 340
      • Chapter 12 : WIDE SCALE DEPLOYMENT OF KNOWLEDGE BASED SYSTEMS / Q. O'Neal
      • 1. Introduction = 344
      • 2. Examples of Leverage from Wide Scale Deployment = 345
      • 3. The First Prerequisite to Propagation : Original Success = 345
      • 3.1. KBS Project Planning = 345
      • 3.2. KBS Application Implementation = 350
      • 4. The KBS Project Database = 351
      • 5. Establishing a Climate of Acceptance = 351
      • 5.1. Preventing the "Not Invented Here" Syndrome = 352
      • 5.2. KBS Language or Shell Selection = 352
      • 5.3. Natural(Human) Language = 353
      • 5.4. Project Awareness Vehicles = 353
      • 6. Distribution and Introduction = 353
      • 6.1 Propagation Types = 353
      • 6.2 Distribution = 354
      • 6.3. Introduction at the Receiving location = 354
      • 7. Maintaining and Extending the Propagated application = 354
      • 8. The Role of an Enterprise-wide Support Organization = 355
      • 9. The Propagation Checklist = 355
      • 10. Conclusion = 355
      • References = 357
      • Appendix A. KBS Propagation Checklist = 358
      • Appendix B. Mini Business Case Project Qualification Process Form = 360
      • Chapter 13 : A CANONICAL MODELING FACILITY FOR CAPTURING KNOWLEDGE IN INFORMATION BASE INTEGRATION / L. Marinos and J.Lee
      • 1. Introduction = 361
      • 2. Issues = 363
      • 3. Existing Approaches = 364
      • 4. The Proposed Approach = 367
      • 4.1. Centralized Clusters Architecture = 367
      • 4.2. The Modeling Facility of the Global System = 371
      • 4.3. A Global Entity Example = 374
      • 4.4. The Functions of the Global Manager = 377
      • 5. Conclusion = 380
      • References = 380
      • Chapter 14 : HIGH LEVEL REASONING IN COOPERATIVE KNOWLEDGE SYSTEMS / F. Zheng and D. Liu
      • 1. Introduction = 383
      • 2. The Bsic Description of Multi-Dimentsio Argument = 386
      • 3. The Calculation of Dependence Value = 390
      • 4. The Calculation of External Constraints = 394
      • 5. The Introduction of Multi-Dimentsion Argument System Framework PAT-1 = 395
      • 6. Conclusion = 397
      • References = 397
      • Chapter 15 : A DATA PARALLEL SHELL FOR LARGE KNOWLEDGE BASES / A. K. Bansal and J. L. Potter
      • 1. Introduction = 399
      • 1.1. Exploiting Data Parallelism - An Associative Solution = 402
      • 2. Background - Conventional Implementation = 405
      • 2.1. Data Structures in Logic Program = 407
      • 3. Problems with Conventional Models = 407
      • 3.1. Problems with Data Representation = 407
      • 3.2. Problems with Control Flow = 408
      • 3.3. What are the Alternatives = 409
      • 4. Associative Computing and data Parallelism = 410
      • 4.1. ASC - An Associative Computing Language= 410
      • 5. Data Parallel Prolog = 414
      • 5.1. Associative Logical data structures = 414
      • 5.2. Representing Logic programs Associatively = 415
      • 6. Goal Reduction = 416
      • 6.1. Pruning the Undesired Clauses = 418
      • 6.2. Identifying Potential Bindings using Data Parallelis = 419
      • 6.3. Determining Matched Data Clauses = 421
      • 6.4. Associative Unification and Its Usage = 421
      • 6.5. Handling Bindings = 422
      • 7. Data Parallel Prolog Shell = 423
      • 7.1. Model Structure = 424
      • 7.2. Environment Management = 424
      • 8. Model Behavior = 426
      • 8.1. Handling Goal Reduction = 426
      • 8.2. Handling Forward Control Flow = 427
      • 8.3. Handling Backtracking = 428
      • 8.4. Handling Cuts = 428
      • 8.5. A Simple Example = 428
      • 8.6. System Predicates and Extra Logical Predicates = 429
      • 9. Conclusion = 429
      • References = 430
      • Chapter 16 : A KNOWLEDGE BASED APPROACH FOR THE SPECIFICATION AND ANALYSIS OF REAL-TIME SOFTWARE SYSTEMS / J.J.P. Tsai and H.-C. Jang
      • 1. Introduction = 432
      • 2. Real Time Requirements Specification Language = 434
      • 2.1. Real-Time System = 434
      • 2.2. Real-Time process = 434
      • 2.3. Timing Constraint = 435
      • 3. RT-FRORL System Model = 436
      • 3.1. Modeling Periodic Process Using RT-FRORL = 437
      • 3.2. Modeling Sporadic Process Using RT-FRORL = 437
      • 3.3. Example = 438
      • 4. Formal Foundation of RT-FRORL = 439
      • 5. Converting RT-FRORL Specification to Its Underlying Logic Formula = 443
      • 6. Requirements Analysis in RT-FROLRL = 444
      • 7. Conclusion and Future Research = 448
      • References = 449
      • SECTION 4 : EVALUATION OF KNOWLEDGE BASES
      • Chapter 17 : A TOOL FOR KNOWLEDGE BASE VERIFICATION / D. Zhang and D. Nguyen
      • 1. Introduction = 455
      • 2. Related Work = 456
      • 3. A Verification Tool = 458
      • 3.1. Inconsistencies and Incompleteness = 458
      • 3.2. Pr/T Net Model of Rule Base = 461
      • 3.3. Syntactic Pattern Recognition = 468
      • 4. Implementation = 469
      • 4.1. Components of PREPARE = 469
      • 4.2. Transformer = 472
      • 4.3. Scanner = 472
      • 4.4. Formulator = 475
      • 4.5. Classifier = 476
      • 5. Discussions = 483
      • 6. Conclusion = 485
      • References = 485
      • Chapter 18 : PRODUCTION GRAPH : A GRAPH THEORETICAL MODEL FOR CHECKING KNOWLEDGE BASE ANOMALIES / E.L. Lim, J. McCallum and K.H. Chan
      • 1. Introduction = 487
      • 1.1. Anomalies in Rule Based Systems = 489
      • 1.2. Knowledge Debugging Tools = 490
      • 1.3. Considerations for Anomaly Checking = 494
      • 1.4. Redefinitions of the Anomalies = 495
      • 1.5. Production-Graph = 495
      • 1.6. A Guide to this Article = 496
      • 2. A Restricted First Order Language for P-Graph = 496
      • 2.1. A Restricted First Order Lnaguage = 497
      • 3. Basic Elements of P-Graph = 500
      • 3.1. A Toy Design Expert System = 500
      • 3.2. Considerations in Developing P-Graph = 502
      • 3.3. Formal definition of P-Graph = 505
      • 3.4. Extension of P-Graph = 509
      • 4. Structures of P-Graph for Anomaly Checks = 511
      • 4.1. P-Graph Structures = 511
      • 4.2. Inference Mechanism of P-Graph = 513
      • 4.3. Advantages of P-Graph = 513
      • 5. Anomalies : Definition and Detectio using P-graph = 514
      • 5.1. Conflicting Facts and Rules = 514
      • 5.2. Incompleteness - Missing Rules = 515
      • 5.3. Redundant Rules = 516
      • 5.4. Subsumed Rules = 517
      • 5.5. Unreachable Rules = 518
      • 5.6. Deadend Rules = 519
      • 5.7. Cyclic Rules = 519
      • 6. Discussion = 520
      • References = 521
      • Chapter 19 : VALIDATION OF NONMONOTONIC KNOWLEDGE-BASED SYSTEMS / C. L. Chang, R. A. Stachowitz and J. B. Combs
      • 1. Introduction = 524
      • 2. Nonmonotonic Knowledge-Based Systems = 526
      • 3. Validation of Nonmonotonic Knowledge-Based Systems = 531
      • 3.1. Redundancy Checking = 531
      • 3.2. Inconsistency Checking = 533
      • 3.3. Incompleteness Checking = 534
      • 4. Concluding Remarks = 535
      • References = 536
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