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Tables of Contents for Artificial Intelligence and Automation
Chapter/Section Title
Page #
Page Count
Preface
v
Section 1: Issues in AI
3
80
CHAPTER 1 A NEW WAY TO ACQUIRE KNOWLEDGE
3
7
H-Y Wang
Abstract
3
1
1. The Knowledge Acquisition and Developing on Resolving Problem
3
1
2. The Thinking Model of Solving a Problem in Social and Economic
4
1
3. The Structuring of the Expert Experience Geared to a Problem
5
1
4. The Structure of the Knowledge Base System Geared to a Problem
6
1
5. Acquiring Knowledge on Problem Resolving
7
2
6. Conclusion
9
1
References
9
1
CHAPTER 2 AN SPN KNOWLEDGE REPRESENTATION SCHEME
10
18
J. Gattiker
N. Bourbakis
Abstract
10
1
1. Introduction
10
1
2. Comparison of Knowledge Representation Methodologies
11
2
3. Petri Nets and Production Rules
13
4
3.1. A Primitive for Representing Knowledge Atoms
13
2
3.2. Representation of Knowledge
15
1
3.3. Example
16
1
3.4. Forward and Backward Chaining
16
1
4. Petri Nets and Logic
17
2
4.1. Extension of Production Rules to Logic
17
1
4.2. Representation of FOPC: Predicate Transition Nets
18
1
5. Petri Nets and Semantic Networks
19
2
6. Examples of Structured Computation Using Petri Nets
21
4
6.1. Object Recognition
21
2
6.2. Planning
23
1
6.3. Predicate Calculus Circuit Simulation
23
2
7. Discussion
25
1
8. Conclusion
26
1
References
26
2
CHAPTER 3 ON THE DEEP STRUCTURES OF WORD PROBLEMS AND THEIR CONSTRUCTION
28
20
F. Gomez
Abstract
28
1
1. Introduction
28
2
2. Schema-Based Understanding
30
2
3. The Deep Structure of Problems
32
4
4. The Deep Structure of Motion Problems
36
3
5. The Construction of the Deep Structure of Motion Problems
39
5
6. The Solution of Motion Problems
44
1
7. Conclusion
45
1
References
46
2
CHAPTER 4 RESOLVING CONFLICTS IN INHERITANCE REASONING WITH STATISTICAL APPROACH
48
16
C.W. Lee
Abstract
48
1
1. Introduction
48
1
2. Interpretations of Defeasible Assertion
49
1
3. Evidential Probabilities
50
1
4. Model
51
1
5. Detecting the Existence of Rules
52
2
6. Inferences
54
5
6.1. Specificity
54
1
6.2. Generality
55
4
7. Algorithm
59
1
8. Examples
60
2
9. Conclusion
62
1
References
62
2
CHAPTER 5 INTEGRATING HIGH AND LOW LEVEL COMPUTER VISION FOR SCENE UNDERSTANDING
64
19
R. Malik
S. So
Abstract
64
1
1. Introduction
64
1
2. Background
64
1
3. Problem Statement
65
1
4. Approach
65
1
5. Graph Extraction Algorithm
66
7
5.1. Vertices Graph
67
1
5.2. Regions Graph
68
4
5.3. Exponent Regions Labeling
72
1
5.4. Duality between Vertices Graph and Regions Graph
73
1
6. Graph Reduction Algorithms
73
3
7. Experiments and Results
76
1
8. Conclusion
77
1
References
78
5
Section 2: AI Tools
83
206
CHAPTER 6 THE EVOLUTION OF COMMERCIAL AI TOOLS: THE FIRST DECADE
83
13
F. Hayes-Roth
1. Introduction
83
1
2. Setting the Stage
83
3
2.1. The Commercial Milieu
83
2
2.2. The Technical Milieu
85
1
3. Examples of Specific Advancements
86
7
3.1. Support for the Task Specialization
86
1
3.2. End-User Knowledge Programming and Maintenance
87
3
3.3. Systems that Support Modularity and Embeddability
90
1
3.4. Support for Co-operative Problem-Solving
91
2
4. Commercial and Technical Objectives for the 90s
93
1
4.1. Current Technological Barriers and Objectives for the 90s
93
1
4.2. Current Commercial Barriers and Objectives for the 90s
94
1
5. Conclusion
94
1
References
95
1
CHAPTER 7 REENGINEERING: THE AI GENERATION -- BILLIONS ON THE TABLE
96
47
J.S. Minor Jr.
Abstract
96
1
1. Unit Continuous Optimization Manufacturing -- Introduction
97
2
2. UCOM World Class Manufacturing Strategies
99
17
2.1. Mass Customization
99
2
2.2. Activity Based Costing
101
1
2.3. Process Reengineering
101
7
2.4. Concurrent Engineering
108
1
2.5. Continuous Flow Manufacturing
109
7
2.6. Strategies Review
116
1
3. UCOM Artificial Intelligence Review
116
10
3.1. Senses
116
4
3.2. Reasoning
120
1
3.3. Neural Networks
121
2
3.4. Hybrid
123
1
3.5. Fuzzy Logic
123
1
3.6. Genetic Algorithms
124
2
3.7. Review
126
1
4. UCOM Simulation Review
126
1
4.1. Review
127
1
5. UCOM Continuous Optimization Manufacturing MINOR I Model
127
9
5.1. Engineering Redesign
128
2
5.2. Engineering Impact
130
1
5.3. Sales/Marketing Impact
130
2
5.4. Plant Floor Impact
132
1
5.5. Employee Impact
133
1
5.6. Profitability Impact
134
1
5.7. Vendor Impact
135
1
5.8. Customer Impact
135
1
5.9. Pilot Project
135
1
6. UCOM Continuous Optimization Manufacturing MINOR II Model
136
5
6.1. Review
139
2
References
141
2
CHAPTER 8 AN INTELLIGENT TOOL FOR DISCOVERING DATA DEPENDENCIES IN RELATIONAL DBS
143
49
P. Gavaskar
F. Golshani
Abstract
143
1
1. Introduction
143
6
1.1. Overview of the Problem
145
1
1.2. Preliminaries
146
3
1.3. Organization of the Chapter
149
1
2. Previous Work
149
2
3. Knowledge Discovery in Databases
151
4
3.1. Statistical Pattern Recognition
151
1
3.2. Neural Networks
152
1
3.3. Machine Learning
153
2
3.4. Choosing a Method
155
1
4. Functional Dependency Algorithm
155
13
4.1. Some Important Concepts
156
1
4.2. Inference Rules for Keys
157
1
4.3. Design of the Functional Dependencies Algorithm
158
10
5. Derivation of the Multivalued and Join Dependencies
168
14
5.1. Some Fundamental Concepts
168
2
5.2. Inference Rules for Multivalued Dependencies
170
1
5.3. The Formal Framework
171
5
5.4. The Algorithm
176
2
5.5. Refinements
178
4
6. Realization of the Tool
182
7
6.1. The User Interface
182
1
6.2. Example
183
3
6.3. Performance Analysis
186
3
7. Conclusion
189
1
References
189
3
CHAPTER 9 A CASE-BASED REASONING (CBR) TOOL TO ASSIST TRAFFIC FLOW
192
15
B. Das
S. Bayles
Abstract
192
1
1. Introduction
192
1
2. Background
193
2
3. Problem Analysis: The CBR Approach
195
1
4. The CBR Prototype Overview
196
5
4.1. Knowledge Engineering
196
2
4.2. Case Representation
198
1
4.3. Case Retrieval
199
2
5. Lessons Learned
201
3
6. Future Work
204
1
7. Conclusion
205
1
References
206
1
CHAPTER 10 A STUDY OF FINANCIAL EXPERT SYSTEM BASED ON FLOPS
207
50
T. Kaneko
K. Takenaka
Abstract
207
1
1. Introduction
208
2
2. Expert System
210
10
2.1. Overview
210
1
2.2. Characteristics of an Expert System
211
1
2.3. Components
212
5
2.4. Applications
217
3
3. Fuzzy Expert System
220
9
3.1. Uncertainty in Expert Systems
220
1
3.2. Fuzzy Sets
220
4
3.3. Fuzzy Numbers
224
2
3.4. Fuzzy Inference
226
1
3.5. Fuzzy Expert System
227
1
3.6. Applications
228
1
4. FLOPS
229
14
4.1. Overview of FLOPS
229
1
4.2. Production System
230
4
4.3. FLOPS Anatomy
234
3
4.4. Confidence Level
237
1
4.5. Representation of Fuzziness in FLOPS
238
3
4.6. Parallel FLOPS
241
2
5. Building a Financial Diagnosis System
243
11
5.1. Overview
243
1
5.2. Wall's Index Method
243
3
5.3. Rules of Diagnosis
246
1
5.4. Financial Data
247
3
5.5. Process of Diagnosis
250
2
5.6. Evaluation of the System
252
2
6. Conclusion
254
1
References
255
2
CHAPTER 11 AN ASSOCIATIVE DATA PARALLEL COMPILATION MODEL FOR TIGHT INTEGRATION OF HIGH PERFORMANCE KNOWLEDGE RETRIEVAL AND COMPUTATION
257
32
A.K. Bansal
Abstract
257
1
1. Introduction
257
4
2. Background
261
3
2.1. Preliminary Definitions and Notations
261
1
2.2. Architecture for Associative Computing
262
1
2.3. Associative Computing Paradigm
262
1
2.4. Representing Data on Associative Architecture
263
1
2.5. Associative Representation of Abstract Data
264
1
3. Overview of Logic Programming Concepts
264
3
3.1. WAM -- Conventional Execution Models
266
1
3.2. Advantages of Associative Computation
267
1
4. An Algebra of Associative Computation
267
5
4.1. Laws of Data Association
269
1
4.2. Laws of Associative Search
269
1
4.3. Laws of Associative Selection
270
1
4.4. Laws of Data Parallel Computation
271
1
4.5. Laws of Associative Update
271
1
5. Structure of the Model
272
3
5.1. Associative Representation of Clause-heads
273
1
5.2. Associative Representation of Global Bindings
274
1
5.3. Storing and Manipulating Bags of Bindings
274
1
5.4. Holding Temporary Values -- A Data Parallel Version
274
1
5.5. Handling Control Flow
274
1
5.6. Handling Aliasing
275
1
6. Integrating Retrieval & Computation
275
2
6.1. Alignment and Data Parallel Computation
275
1
6.2. Alignment and Data Management
275
1
6.3. Alignment and Aliasing
276
1
6.4. Alignment and Associative Goal Reduction
276
1
6.5. Deriving Unspecified Relations for Objects
277
1
7. The Model Behavior
277
4
7.1. Handling Aliased Variables
278
3
7.2. Handling Multiple Procedures
281
1
8. Performance Evaluation
281
1
9. Other Related Works
282
1
10. Conclusion
283
1
References
283
6
Section 3: Automation
289
150
CHAPTER 12 SOFTWARE AUTOMATION: FROM SILLY TO INTELLIGENT
289
8
J.-F. Xu
D.-X. Chen
Z.-J. Wang
L.-J. Dong
Abstract
289
1
1. Introduction
289
1
2. Incorporating Machine Learning into Software Automation
290
5
2.1. Inductive Program Synthesis
290
2
2.2. Machine Learning in Inductive Problem Solving
292
3
2.3. Analogical Program Derivation
295
1
3. Future Work
295
1
3.1. Putting Experimental Systems on the Design Level into Practice
295
1
3.2. Designing a New System
295
1
3.3. Looking for Analogical Approach
295
1
References
296
1
CHAPTER 13 SOFTWARE ENGINEERING USING ARTIFICIAL INTELLIGENCE: THE KNOWLEDGE BASED SOFTWARE ASSISTANT
297
18
D. White
Abstract
297
1
1. Background
297
3
1.1. Opportunity
297
1
1.2. The Software Problem
298
1
1.3. Expert Systems and Automatic Programming
299
1
2. An AI Approach
300
13
2.1. Failings of Conventional Approaches
300
3
2.2. Objectives
303
1
2.3. Concept
303
1
2.4. Approach
304
3
2.5. Paradigm Scenario
307
2
2.6. KBSA Program
309
4
3. Conclusion
313
1
References
313
2
CHAPTER 14 KNOWLEDGE BASED DERIVATION OF PROGRAMS FROM SPECIFICATIONS
315
33
T. Weight
J. Boyle
T. Harmer
F. Weil
Abstract
315
1
1. Introduction
315
1
2. The Software Development Process
316
2
2.1. Idealized Software Development Process
317
1
2.2. Practical Software Development Process
317
1
3. What is Program Transformation?
318
2
4. An Experiment in Program Transformation Applied to a Commercial Product
320
8
4.1. Implementing Transcription to Hardware Specific Operations
320
1
4.2. Implementing High Level Abstract Data Types
321
2
4.3. Implementing Separation of Concerns
323
2
4.4. Implementing High Level Specification Constructs
325
3
5. Conclusion and Future Work
328
1
References
329
1
Appendix
330
18
CHAPTER 15 AUTOMATIC FUNCTIONAL MODEL GENERATION FOR PARALLEL FAULT DESIGN ERROR SIMULATIONS
348
30
S.-E. Chang
S.A. Szygenda
Abstract
348
1
1. Introduction
348
1
2. AFMG Domain Analysis
349
9
2.1. Signal Modeling Phase
350
1
2.2. Primitive Modeling Phase
350
3
2.3. Functional Modeling Phase
353
4
2.4. The Domain Independent Programming Knowledge
357
1
3. User Interface and Specification Tools
358
5
4. Behavioral Domain Model Generation -- Automatic Primitive Modeling
363
6
5. Structural Domain Model Generation -- Automatic Functional Modeling
369
5
6. Experiment Results
374
1
7. Conclusion
375
1
References
376
2
CHAPTER 16 VISUAL REVERSE ENGINEERING USING SPNs FOR AUTOMATED DIAGNOSIS AND FUNCTIONAL SIMULATION OF DIGITAL CIRCUITS
378
13
J. Gattiker
S. Mertoguno
Abstract
378
1
1. Introduction
378
1
2. The Detection Diagnosis Methodology
379
2
2.1. Visual Reverse Engineering
379
2
3. Converging Functional and Structural Knowledge with SPNs
381
2
3.1. Definitions and Primitive for SPN Knowledge Representation
383
1
3.2. Mapping Graphs onto SPNs for Knowledge Processing
383
1
4. An Illustrative Example
383
7
5. Conclusion
390
1
References
390
1
CHAPTER 17 THE IMPACT OF AI IN VLSI DESIGN AUTOMATION
391
24
M. Mortazavi
N. Bourbakis
Abstract
391
1
1. Introduction
391
7
1.1. VLSI Design Automation
392
1
1.2. Top-Down Design Methodology for the DA Process
392
4
1.3. Classification of AI Tools
396
2
2. Roles of AI Tools in VLSI Design Automation
398
2
2.1. Expert Systems in Design Automation Process
398
1
2.2. Rule Based Systems
398
1
2.3. Heuristic Algorithms in VLSI Design Automation
399
1
2.4. Learning Algorithms in VLSI Design Automation
399
1
3. AI Tools in Design Automation
400
10
3.1. Synthesis Tool Systems
400
5
3.2. Evaluation/Analysis Tools
405
1
3.3. Design Environment/Management System Tools
406
4
4. Concluding Remarks
410
1
References
411
4
CHAPTER 18 THE AUTOMATED ACQUISITION OF SUBCATEGORIZATIONS OF VERBS, NOUNS AND ADJECTIVES FROM SAMPLE SENTENCES
415
24
F. Gomez
Abstract
415
1
1. Introduction
415
1
2. Overview of the Parser
416
5
3. The Acquisition of the Symantic Usages
421
2
4. Learning the Subcategorization of Verbs
423
1
5. Merging the Segment Usages into a Final Usage
424
2
6. Learning the Subcategorization of Nouns and Adjectives
426
2
7. Ambiguity and Other Problems
428
1
8. Related Work
429
1
9. Conclusion
430
1
References
431
1
Appendix
432
7
Section 4: Planning
439
CHAPTER 19 GENERAL METHOD FOR PLANNING AND RENDEZVOUS PROBLEMS
439
27
K.I. Trovato
Abstract
439
1
1. Introduction
439
1
2. Framework
439
4
3. Algorithm Analysis/Accuracy
443
1
4. The Fire Exit Problem
444
1
5. The Robot Path Planning Problem
445
1
6. The Vehicle Maneuvering Problem
446
5
7. A Forward-Only Constraint
451
1
8. Use of the System for Complex Maneuvers and Larger Areas
452
2
9. A Radio Controlled Example
454
1
10. High Speed Vehicle Maneuvering
455
2
11. Planning the Coordination of Multiple Actors -- Synergistic Planning
457
7
11.1. Example Coordination Problem
459
4
11.2. Coordination of Fleet Trucks
463
1
11.3. Brief Analysis
463
1
11.4. Other Applications
463
1
12. Invitation
464
1
13. Conclusion
464
1
References
465
1
CHAPTER 20 LEARNING TO IMPROVE PATH PLANNING PERFORMANCE
466
37
P.C. Chen
Abstract
466
1
1. Introduction
466
2
2. Related Work
468
1
3. Algorithmic Framework
469
3
4. General Analysis
472
5
5. A Specific Case Analysis
477
4
6. Particular Analysis
481
8
6.1. Pessimistic Model
483
4
6.2. Randomized Model
487
2
7. Application and Verification
489
3
7.1. Pessimistic Model
489
1
7.2. Randomized Model
490
2
8. Extension to Changing Environments
492
7
8.1. Object-Attached Experience Abstraction
494
1
8.2. On-Demand Experience Repair
495
1
8.3. Other Repair Strategies
495
1
8.4. Solution Quality and Redundancy
496
1
8.5. Example
496
2
8.6. Computational Experience
498
1
9. Conclusion
499
1
References
500
3
CHAPTER 21 INCREMENTAL ADAPTATION AS A METHOD TO IMPROVE REACTIVE BEHAVIOR
503
19
A.J. Hendriks
D.M. Lyons
Abstract
503
1
1. Introduction
503
2
2. Reactive Behavior
505
3
2.1. Pengi: Intelligence Through Interaction
505
1
2.2. The Subsumption Architecture
506
1
2.3. Design Reactive Machines
507
1
3. Incremental Adaptation
508
3
3.1. The Incremental Adaptation Methodology
509
2
4. Incremental Adaptation Architectures
511
8
4.1. THEO: Planning on Demand
511
1
4.2. DYNA: Learning Control Policies
512
1
4.3. ERE: Generation of Situated Control Rules for Action Selection
513
2
4.4. XFRM: Planning by Transformation
515
1
4.5. ADAPT: Planning and Reacting
516
3
5. Summary
519
1
References
520
2
CHAPTER 22 AN SPN-NEURAL PLANNING METHODOLOGY FOR COORDINATION OF MULTIPLE ROBOTIC ARMS WITH CONSTRAINED PLACEMENT
522
N. Bourbakis
A. Tascillo
Abstract
522
1
1. Introduction
522
1
2. Notations and Definitions
523
1
3. SPN Planning Methodology
523
5
3.1. Why Stochastic Petri-Nets
523
1
3.2. SPN Model
524
1
3.3. Theoretical Aspects of the SPN Planning Model
524
1
3.4. SPN Planning in UD Using two Robotic Arms
525
3
4. An Illustrative Example of the SPN Planning with a Self-Organized Neural Network
528
8
5. Conclusion
536
1
References
536