Artificial intelligence in industrial decision making, control and automation:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Dordrecht [u.a.]
Kluwer
1995
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| Series: | International series on microprocessor-based and intelligent systems engineering
14 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXIX, 767 S. graph. Darst. |
| ISBN: | 0792333209 |
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MARC
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| 245 | 1 | 0 | |a Artificial intelligence in industrial decision making, control and automation |c ed. by Spyros G. Tzafestas ... |
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| 650 | 4 | |a Intelligent control systems | |
| 650 | 4 | |a Automation | |
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| adam_text | ARTIFICIAL INTELLIGENCE IN INDUSTRIAL DECISION MAKING, CONTROL AND
AUTOMATION EDITED BY SPYROS G. TZAFESTAS DEPARTMENT OFELECTRICAL AND
COMPUTER ENGINEERING, NATIONAL TECHNICAL UNIVERSITY OF ATHENS, ATHENS,
GREECE AND HENK B. VERBRUGGEN DEPARTMENT OFELECTRICAL ENGINEERING, DELFT
UNIVERSITY OF TECHNOLOGY, DELFT, THE NETHERLANDS KLUWER ACADEMIC
PUBLISHERS DORDRECHT / BOSTON / LONDON CONTENTS PREFACE CONTRIBUTORS
PART 1 GENERAL ISSUES CHAPTER1 ARTIFICIAL INTELLIGENCE IN INDUSTRIAL
DECISION MAKING, CONTROL AND AUTOMATION: AN INTRODUCTION S. TZAFESTAS
AND H. VERBRUGGEN 1. INTRODUCTION ^ 2. DECISION MAKING, CONTROL AND
AUTOMATION ,2 2.1. DECISION MAKING THEORY 2 2.2. CONTROL AND AUTOMATION
4 3. ARTIFICIAL INTELLIGENCE METHODOLOGIES 6 3.1 REASONING UNDER
UNCERTAINTY 7 3.2 QUALITATIVE REASONING 14 3.3 NEURAL NETS REASONING ^
4. ARTIFICIAL INTELLIGENCE IN DECISION MAKING 19 5. ARTIFICIAL
INTELLIGENCE IN CONTROL AND SUPERVISION ...22 6. ARTIFICIAL INTELLIGENCE
IN ENGINEERING FAULT DIAGNOSIS 24 7. ARTIFICIAL INTELLIGENCE IN ROBOTIC
AND MANUFACTURING SYSTEMS 26 8. CONCLUSIONS * REFERENCES TI VI CHAPTER 2
CONCEPTUAL INTEGRATION OF QUALITATIVE AND QUANTITATIVE PROCESS MODELS E.
A. WOODS 1. INTRODUCTION 41 2. QUALITATIVE REASONING 42 2.1. COMMON
CONCEPTS 43 2.2. QUALITATIVE MATHEMATICS 44 2.3. THE NOTION OF STATE 45
2.4. DESCRIBING BEHAVIOUR 45 2.5. COMPONENTS OF QUALITATIVE REASONING 45
2.6. TOWARDS MORE QUANTITATIVE MODEIS 47 3. FORMAL CONCEPTS AND
RELATIONS IN THE HPT 48 3.1. QUANTITIES 48 3.2. PHYSICAL OBJECTS,
PROCESS EQUIPMENT, MATERIALS AND SUBSTANCES 48 3.3. THE INPUT FILE 49
3.4. ACTIVITY CONDITIONS 49 3.5. NUMERICAL FUNCTIONS AND INFLUENCES 50
3.6. LOGICAL RELATIONS AND RULES 52 4. DEFINING VIEWS AND PHENOMENA 52
4.1. INDIVIDUAIS AND INDIVIDUAL CONDITIONS 52 4.2. QUANTITY CONDITIONS
AND PRECONDITIONS 54 4.3. RELATIONS 55 4.4. DYNAMIC INFLUENCES 56 4.5.
INSTANTIATING A DEFINITION 57 4.6. ACTIVITY LEVELS 57 5. DERIVING AND
REASONING WITH AN HPT MODEL 59 5.1. EXTENDING THE TOPOLOGICAL MODEL 59
5.2. DERIVING THE PHENOMENOLOGICAL MODEL 60 5.3. ACTIVITY AND STATE
SPACE MODEIS 61 6. DISCUSSION AND CONCLUSION 63 REFERENCES 64 VII
CHAPTER 3 TIMING PROBLEMS AND THEIR HANDLING AT SYSTEM INTEGRATION L.
MOTUS ; 1. INTRODUCTION 57 2. ESSENTIAL FEATURES OF CONTROL SYSTEMS 68
2.1. ESSENTIAL (FORCED) CONCURRENCY 70 2.2. TRULY ASYNCHRONOUS MODE OF
EXECUTION OF INTERACTING PROCSSES 70 2.3. TIME-SELECTIVE INTERPROCESS
COMMUNICATION 71 3. CONCERNING TIME-CORRECT FUNCTIONING OF SYSTEMS 71
3.1. PERFORMANCE-BOUND PROPERTIES 72 3.2. TIMEWISE CORRECTNESS OF EVENTS
AND DATA 72 3.3. TIME CORRECTNESS OF INTERPROCESS COMMUNICATION 73 4. A
MATHEMATICAL MODEL FOR QUANTITATIVE TIMING ANALYSIS (Q-MODEL) 73 4.1.
PARADIGMS USED ....... 74 4.2. THE Q-MODEL 74 5. THE Q-MODEL BASED
ANALYTICAL STUDY OF SYSTEM PROPERTIES 76 5.1. SEPARATE ELEMENTS OF A
SPECIFICATION 76 5.2. PAIRS OF INTERACTING PROCESSES 77 5.3. GROUP OF
INTERACTING PROCESSES .......78 6. AN EXAMPLE OF THE Q-MODEL APPLICATION
79 7. CONCLUSIONS G5 REFERENCES G5 CHAPTER 4 ANALYSIS FOR CORRECT
REASONING IN INTERACTIVE MAN ROBOT SYSTEMS: DISJUNCTIVE SYLLOGISM WITH
MODUS PONENS AND MODUS TOLLENS E. C. KOENIG 1. INTRODUCTION GG 2. VALID
COMMAND ARGUMENTS 90 VU1 3. CORRECT REASONING: DISJUNCTIVE SYLLOGISM 91
3.1. PLAUSIBLE COMPOSITE COMMAND ARGUMENTS 92 3.2. PLAUSIBLE COMPOSITE
COMMANDS 92 4. CONCLUSIONS 96 REFERENCES 96 PART 2 INTELLIGENT SYSTEMS
CHAPTER 5 APPLIED INTELLIGENT CONTROL SYSTEMS R. SHOURESHI, M. WHEELER
AND L. BRACKNEY 1. INTRODUCTION 101 2. A PROPOSED STRUCTURE FOR
INTELLIGENT CONTROL SYSTEMS (ICS) 102 3. INTELLIGENT AUTOMATIC
GENERATION CONTROL (IAGC) 105 4. INTELLIGENT COMFORT CONTROL SYSTEM 110
5. CONTROL SYSTEM DEVELOPMENT 111 6. EXPERIMENTAL RESULTS 116 7.
CONELUSION 116 REFERENCES 119 CHAPTER 6 INTELLIGENT SIMULATION IN
DESIGNING COMPLEX DYNAMIC CONTROL SYSTEMS F. ZHAO 1. INTRODUCTON 127 2.
THE CONTROL ENGINEER S WORKBENCH 128 IX 3. AUTOMATIC CONTROL SYNTHESIS
IN PHASE SPACE 128 3.1. OVERVIEW OF THE PHASE SPACE NAVIGATOR 129 3.2.
INTELLIGENT NAVIGATION IN PHASE SPACE 129 3.3. PLANNING CONTROL PATHS
WITH FLOW PIPES 130 4. THE PHASE SPACE NAVIGATOR 131 4.1. REFERENCE
TRAJECTORY GENERATION 131 4.2. REFERENCE TRAJECTORY TRACKING 133 4.3.
THE AUTONOMOUS CONTROL SYNTHESIS ALGORITHMS 135 4.4. DISCUSSION OF THE
SYNTHESIS ALGORITHMS 137 5. AN ILLUSTRATION: STABILIZING A BUECKLING
COLUMN 139 5.1. THE COLUMN MODEL 140 5.2. EXTRACTING AND REPRESENTING
QUALITATIVE PHASE-SPACE STRUCTURE OF THE BUCKLING COLUMN 141 5.3.
SYNTHESIZING CONTROL LAWS FOR STABILIZING THE COLUMN 143 5.4. THE
PHASE-SPACE MODELING MAKES THE GLOBAL NAVIGATION POSSIBLE 148 6. AN
APPLICATION: MAGLEV CONTROLLER DESIGN 148 6.1. THE MAGLEV MODEL 148 6.2.
PHASE-SPACE CONTROL TRAJECTORY DESIGN 150 7. DISCUSSIONS 155 8.
CONCLUSIONS 155 REFERENCES 156 CHAPTER 7 MULTIRESOLUTIONAL ARCHITECTURES
FOR AUTONOMOUS SYSTEMS WITH INCOMPLETE AND INADEQUATE KNOWLEDGE
REPRESENTATION A. MEYSTEL 1. INTRODUCTION I59 2. ARCHITECTURES FOR
INTELLIGENT CONTROL SYSTEMS: TERMINOLOGY, ISSUES, AND A CONCEPTUAL
FRAMEWORK 161 2.1. DEFINITIONS 161 2.2. ISSUES AND PROBLEMS 165 X 2.3.
CONCEPTUAL FRAMEWORK FOR INTELLIGENT SYSTEMS ARCHITECTURE 170 3.
OVERVIEW OF THE GENERAL RESULTS 171 4. EVOLUTION OF THE
MULTIRESOLUTIONAL CONTROL ARCHITECTURE (MCA): ITS ACTIVE AND REACTIVE
COMPONENTS 173 4.1. GENERAL STRUCTURE OF THE CONTROLLER 173 4.2.
MULTIRESOLUTIONAL CONTROL ARCHITECTURE (MCA) 175 5. NESTED CONTROL
STRATEGY: GENERATION OF A NESTED HIERARCHY FOR MCA 177 5.1. GFACS
TRIPLET: GENERATION OF INTELLIGENT BEHAVIOR 177 5.2. OFF-LINE DECISION
MAKING PROCEDURES OF PLANNING-CONTROL IN MCA 178 5.3. GENERALISED
CONTROLLER 180 5.4. UNIVERSE OF THE TRAJECTORY GENERATOR: SECOND LEVEL
181 5.5. REPRESENTATION OF THE PLANNING/CONTROL PROBLEM IN MCA 183 5.6.
SEARCH AS THE GENERAL CONTROL STRATEGY FOR MCA 185 6. ELEMENTS OF THE
THEORY OF NESTED MULTIRESOLUTIONAL CONTROL FOR MCA 187 6.1. COMMUTATIVE
DIAGRAM FOR A NESTED MULTIRESOLUTIONAL CONTROLLER 187 6.2. TESSELLATED
KNOWLEDGE BASES 187 6.3. GENERALIZATION 188 6.4. ATTENTION AND
CONSECUTIVE REFINEMENT 189 6.5. ACCURACY AND RESOLUTION OF
REPRESENTATION 190 6.6. COMPLEXITY AND TESSELLATION: E-ENTROPY 194 7.
MCA IN AUTONOMOUS CONTROL SYSTEM 195 7.1. THE MULTIRESOLUTIONAL
GENERALIZATION OF SYSTEM MODEIS 195 7.2. PERCEPTION STRATIFIED BY
RESOLUTION 196 7.3. MAPS OF THE WORLD STRATIFIED BY RESOLUTION 197 8.
DEVELOPMENT OF ALGORITHMS FOR MCA 198 8.1. EXTENSIONS OF THE
BELLMAN SOPTIMALITY PRINCIPLE 198 8.2. NESTED MULTIRESOLUTIONAL SEARCH
IN THE STATE SPACE 198 9. COMPLEXITY OF KNOWLEDGE REPRESENTATION AND
MANIPULATION 201 9.1. MULTIRESOLUTIONAL CONSECUTIVE REFINEMENT: SEARCH
IN THE STATE SPACE 201 9.2. MULTIRESOLUTIONAL CONSECUTIVE REFINEMENT:
MULTIRESOLUTIONAL SEARCH OF A TRAJECTORY IN THE STATE SPACE 203 9.3.
EVALUATION AND MINIMIZATION OF THE COMPLEXITY OF THE MCA 205 10.
CASESTUDIES 208 10.1 A PILOT FOR AN AUTONOMOUS ROBOT (TWO LEVELS OF
RESOLUTION) 208 XI 10.2 PILOT WITH TWO AGENTS FOR CONTROL (A CASE OF
BEHAVIORAL DUALITY) 211 11. CONCLUSION ...219 REFERENCES ...220 CHAPTER
8 DISTRIBUTED INTELLIGENT SYSTEMS IN CELLULAR ROBOTICS T. FUKUDA, T.
UEYAMA AND K. SEKIYAMA 1. INTRODUCTION 225 2.CONCEPT OF CELLULAR ROBOTIC
SYSTEM 226 3. PROTOTYPES OF CEBOT 227 3.1. PROTOTYPE CEBOT MARK IV 229
3.2. CELLULAR MANIPULATOR 231 4. DISTRIBUTED GENETIC ALGORITHM 234 4.1.
DISTRIBUTED DECISION MAKING 234 4.2. STRUCTURE CONFIGURATION PROBLEM 235
4.3. APPLICATION OF GENETIC ALGORITHM 236 4.4. DISTRIBUTED GENETIC
ALGORITHM 239 4.5. SIMULATION RESULTS 241 5. CONCLUSIONS 245 REFERENCES
245 CHAPTER 9 DISTRIBUTED ARTIFICIAL INTELLIGENCE IN MANUFACTURING
CONTROL S. ALBAYRAK AND H. KRALLMANN 1. INTRODUCTION 247 2. TASKS OF
MANUFACTURING CONTROL 248 3. THE STATE-OF-THE-ART OF THE DAI TECHNIQUE
IN MANUFACTURING CONTROL 252 3.1. ISIS/OPIS 9S9 XOE 3.2. SOJA/SONIA 254
3.3. YAMS 255 4. DISTRIBUTED ARTIFICIAL INTELLIGENCE 259 4.1.
COOPERATIVE PROBLEM SOLVING 261 4.2. PHASES OF COOPERATING PROBLEM
SOLVING 261 4.3. BLACKBOARD METAPHOR, MODEL AND FRAMEWORKS 264 4.4.
HISTORY OF THE BLACKBOARD MODEL 274 4.5. ADVANTAGES OF DAI 276 5.
VERFLEX - BB SYSTEM: APPROACH AND IMPLEMENTATION 277 5.1. DISTRIBUTED
APPROACH TO THE SOLUTION OF THE TASK ORDER EXECUTION 277 5.2. WHY WAS
THE BLACKBOARD MODEL USED? 281 5.3. THE VERFLEX - BB SYSTEM 281
REFERENCES 292 PART 3 NEURAL NETWORKS IN MODELLING, CONTROL AND
SCHEDULING CHAPTER 10 ARTIFICIAL NEURAL NETWORKS FOR MODELLING A.J.
KRIJGSMAN, H.B. VERBRUGGEN AND P.M. BRUIJN 1. INTRODUCTION 297 2.
DESCRIPTION OF ARTIFICIAL NEURONS 298 3. ARTIFICIAL NEURAL NETWORKS
(ANN) 299 4. NONLINEAR MODEIS AND ANN 300 5. NETWORKS 302 5.1.
MTILTILAYERED STATIC NEURAL NETWORKS 302 5.2. RADIAL BASIS FUNCTION
NETWORKS 303 5.3. CEREBELLUM MODEL ARTICULATION CONTROLLER (CMAC) 304 6.
IDENTIFICATION OF DYNAMIC SYSTEMS USING ANN 306 XIII 6.1. IDENTIFICATION
PROBLEM DEFMITION 306 6.2. MODEIDESCRIPTION FORIDENTIFICATION 308 7.
HYBRID MODELLING 308 ORTHOGONAL LEAST-SQUARES ALGORITHM.; 309 8. MODEL
VALIDATION 313 9. EXPERIMENTS AND RESULTS USING NEURAL IDENTIFICATION
314 10. CONCLUSIONS 323 REFERENCES 323 CHAPTER11 NEURAL NETWORKS IN
ROBOT CONTROL S.G. TZAFESTAS 1. INTRODUCTION 327 2. NEUROCONTROL
ARCHITECTURES 328 2.1. GENERAL ISSUES 328 2.2. UNSUPERVISED NN CONTROL
ARCHITECTURES 329 2.3. DIMA II. NEUROCONTROLLER FOR LINEAR SYSTEMS 331
2.4. ADAPTIVE LEARNING NEUROCONTROL FOR CARMA SYSTEMS 336 3. ROBOT
NEUROCONTROL 339 3.1. A LOOK AT ROBOTICS 339 3.2. NEURAL NETS IN
ROBOTICS: GENERAL REVIEW 341 3.3. ROBOT CONTROL USING HIERARCHICAL NNS
343 3.4. MINIMUM TORQUE-CHANGE ROBOT NEUROCONTROL 346 3.5. IMPROVED
ITERATIVE LEARNING ROBOT NEUROCONTROLLER 349 4. NUMERICAL EXAMPLES 352
4.1. EXAMPLE 1: DIMA II CONTROLLER FOR LINEAR SYSTEMS 352 4.2. EXAMPLE
2: NEUROCONTROLLER FOR CARMA SYSTEMS 354 4.3. EXAMPLE 3: SUPERVISED
NEUROCONTROL OF A BROOM - BALANCING SYSTEM 357 4.4. EXAMPLE 4: FEEDBACK
- ERROR LEARNING ROBOT NEUROCONTROL 361 4.5. EXAMPLE 5: ITERATIVE ROBOT
NEUROCONTROL 366 4.6. EXAMPLE 6: UNSUPERVISED ROBOT-NEUROCONTROLLER
USING HIERARCHICAL NN 372 5. CONCLUSIONS AND DISCUSSION 375 XIV 6.
APPENDIX: A BRIEF LOOK AT NEURAL NETWORKS 376 6.1. SINGLE - LAYER
PERCEPTRON (SLP) 377 6.2. MULTI - LAYER PERCEPTRON (MLP) 37G 6.3.
HOPFIELD NETWORK 3G J REFERENCES 3G4 CHAPTER 12 CONTROL STRATEGY OF
ROBOTIC MANIPULATOR BASED ON FLEXIBLE NEURAL NETWORK STRUCTURE M.
TESHNEHLAB AND K. WATANABE 1. INTRODUCTION 3G9 2. THE REPRESENTATION OF
BIPOLAR UNIT FUNCTION ..390 3. LEARNING ARCHITECTURE 391 3.1. THE
LEARNING OF CONNECTION WEIGHTS 392 3.2. THE LEARNING OF SIGMOID UNIT
FUNCTION PARAMETERS 393 4. NEURAL NETWORK - BASED ADAPTIVE CONTROLLER
394 4.1. THE FEEDBACK - ERROR LEARNING RULE 395 4.2. ADAPTATION OF
NEURAL NETWORK CONTROLLER 396 5. SIMULATION EXAMPLE 39-7 6. CONCLUSION
4Q2 REFERENCES 4Q2 CHAPTER 13 NEURO - FUZZY APPROACHES TO ANTICIPATORY
CONTROL LXI. TSOUKALAS, A. IKONOMOPOULOS AND R.E. UHRIG 1. INTRODUCTION
4Q5 2. ISSUESOFFORMALISM ANTICIPATORY SYSTEMS 407 3. ISSUES OF
MEASUREMENT AND PREDICTION 412 4. CONCLUSIONS 417 REFERENCES 410 XV
CHAPTER 14 NEW APPROACHES TO LARGE - SCALE SCHEDULING PROBLEMS:
CONSTRAINT DIRECTED PROGRAMMING AND NEURAL NETWORKS Y. KOBAYASHI AND H.
NONAKA 1. INTRODUCTION 42I 2. METHOD .......422 2.1. PROBLEM AND METHOD
DESCRIPTION 422 2.2. KNOWLEDGE - BASED METHOD FOR LOWER - LEVEL PROBLEMS
424 2.3. KNOWLEDGE - BASED SCHEDULING METHOD FOR UPPER- LEVEL PROBLEMS
431 2.4. NEURAL NETWORKS FOR UPPER - LEVEL PROBLEMS 432 3. APPLICATION
EXAMPLES 439 3.1. SCHEDULING SYSTEMS .....439 3.2. PROBLEM 439 3.3.
RESULTS 439 4. CONCLUSIONS 444 REFERENCES 445 PART 4 SYSTEM DIAGNOSTICS
CHAPTER 15 KNOWLEDGE - BASED FAULT DIAGNOSIS OF TECHNOLOGICAL SYSTEMS H.
VERBRUGGEN, S. TZAFESTAS AND E. ZANNI 1. INTRODUCTION 449 2. KNOWLEDGE
REPRESENTATION AND ACQUISITION FOR FAULT DIAGNOSIS 451 2.1. KNOWLEDGE
REPRESENTATION 451 XVI 2.2. KNOWLEDGE ACQUISITION 454 3. FIRST -AND
SECOND - GENERATION DIAGNOSTIC EXPERT SYSTEMS 456 3.1. GENERAL ISSUES
456 3.2. FIRST- GENERATION EXPERT SYSTEMS 456 3.3. DEEP REASONING 457
3.4. QUALITATIVE REASONING 458 3.5. SECOND - GENERATION EXPERT SYSTEMS
462 4. A GENERAL LOOK AT THE FD METHODOLOGIES AND SECOND - GENERATION ES
ARCHITECTURES 462 4.1. GENERAL ISSUES 462 4.2. DIAGNOSTIC MODELLING 463
4.3 SECOND - GENERATION FD EXPERT SYSTEM ARCHITECTURES 464 5. A SURVEY
OF DIGITAL SYSTEMS DIAGNOSTIC TOOLS 467 5.1.THED-ALGORITHM 467 5.2.
DAVIS DIAGNOSTIC METHODOLOGY 468 5.3. INTEGRATED DIAGNOSTIC MODEL (IDM)
470 5.4. THE DIAGNOSTIC ASSISTANCE REFERENCE TOOL (DART) 472 5.5 THE
INTELLIGENT DIAGNOSTIC TOOL (IDT) 474 5.6. THE LOCKHEED EXPERT SYSTEM
(LES) 476 5.7. OTHER SYSTEMS 476 6. A GENERAL METHODOLOGY FOR THE
DEVELOPMENT OF FD TOOLS IN THE DIGITAL CIRCUITS DOMAIN 477 6.1.
DESCRIPTION OF THE STRUCTURE 478 6.2. DESCRIPTION OF THE BEHAVIOUR 479
6.3. THE DIAGNOSTIC MECHANISM 480 6.4. THE CONSTRAINT SUSPENSION
TECHNIQUE 482 6.5. ADVANTAGES OF THE DEVIATION DETECTION AND CONSTRAINT
SUSPENSION TECHNIQUE 485 7. A GENERAL METHODOLOGY FOR THE DEVELOPMENT OF
FD TOOLS IN THE PROCESS ENGINEERING DOMAIN 486 8. IMPLEMENTATION OF A
DIGITAL CIRCUITS DIAGNOSTIC EXPERT SYSTEM (DICIDEX) 489 8.1.INTRODUCTION
489 8.2. DICIDEX DESCRIPTION 490 8.3. EXAMPLES OF SYSTEM - USER
DIALOGUES 496 XVU 9. CONCLUSIONS 501 REFERENCES 502 CHAPTER 16 MODEL -
BASED DIAGNOSIS: STATE TRANSITION EVENTS AND CONSTRAINT EQUATIONS K.-E.
ARZEN, A. WALLEN AND T.F. PETTI 1. INTRODUCTION 507 2. DIAGNOSTIC MODEL
PROCESSOR METHOD (DMP) 509 3. MODEL INTEGRATED DIAGNOSIS ANALYSIS SYSTEM
(MIDAS) 512 3.1. MIDAS MODEIS 512 3.2. MIDAS DIAGNOSIS 515 4. STERITHERM
DIAGNOSIS 518 4.1. DMP STERITHERM DIAGNOSIS 518 4.2. MIDAS STERITHERM
DIAGNOSIS 519 5. COMPARISONS 520 6. CONCLUSIONS 522 REFERENCES 523
CHAPTER 17 DIAGNOSIS WITH EXPLICIT MODELS OF GOALS AND FUNCTIONS J.E.
LARSSON 1. INTRODUCTION 525 2. BASIC IDEAS IN MULTILEVEL FLOW MODELING
(MFM) 526 3. AN EXAMPLE OF A FLOW MODEL 526 4. THREE DIAGNOSTIC METHODS
528 4.1 MEASUREMENT VALIDATION 529 4.2. ALARM ANALYSIS 530 4.2. FAULT
DIAGNOSIS 531 XVIII 5. IMPLEMENTATION ...531 6. COMPLEX SYSTEMS 532 7.
CONCLUSIONS 532 REFERENCES 533 PART 5 INDUSTRIAL ROBOTIC, MANUFACTURING
AND ORGANIZATIONAL SYSTEMS CHAPTER 18 MULTI-SENSOR INTEGRATION FOR
MOBILE ROBOT NAVIGATION A.TRACA DE ALMEIDA, H. ARAUJO, J. DIAS AND U.
NUNES 1. INTRODUCTION 537 2. SENSOR-BASED NAVIGATION 537 3. SENSORY
SYSTEM 53G 4. SENSOR INTEGRATION FOR LOCALIZATION : SOME METHODOLOGIES
540 4.1. DATA INTEGRATION - INTRINSIC SENSOR LEVEL 542 4.2. DATA
INTEGRATION - EXTRINSIC SENSOR LEVEL 544 5. EXPERIMENTAL SETUP 547 5.1.
SENSORS DESCRIPTIONS 547 6. CONCLUSIONS 553 REFERENCES CCO XIX CHAPTER
19 INCREMENTAL DESIGN OF A FLEXIBLE ROBOTIC ASSEMBLY CELL USING REACTIVE
ROBOTS E.S. TZAFESTAS AND S.G. TZAFESTAS 1. INTRODUCTION 555 2.
DESCRIPTION OF THE ASSEMBLY CELL 556 3. BASIC ARCHITECTURE OF THE ROBOT
559 4. CASE 1: THEMINIMAL ASSEMBLY CELL 561 5. CASE 2: EXTENDING THE
ROBOTS ARCHITECTURE 562 6. CASE 3: USING MORE THAN ONE ASSEMBLY ROBOTS
563 7. CASE 4: COMBINING CASES 2 AND 3-INTERACTING FACTORS 565 8. CASE
5: THE ADAPTIVE ROBOT - COMMITMENT TO PRODUCT 567 9. CONCLUSIONS AND
FURTHER WORK 569 REFERENCES 570 CHAPTER 20 ON THE COMPARISON OF AI AND
DAI BASED PLANNING TECHNIQUES FOR AUTOMATED MANUFACTURING SYSTEMS A.I.
KOKKINAKI AND K.P. VALAVANIS 1. INTRODUCTION 573 2. TRADITIONAL
ARTIFICIAL INTELLIGENCE PLANNING SYSTEMS 575 2.1. THEOREM PROVING BASED
PLANNING SYSTEMS 577 2.2. BLACKBOARD-BASED ARCHITECTURES 579 2.3.
ASSEMBLY PLANNING AND ASSEMBLY SEQUENCES REPRESENTATIONS 582 3.
DISTRIBUTED ARTIFICIAL INTELLIGENCE PLANNING SYSTEMS 593 3.1.
COORDINATION IN MULTI-AGENT PLANNING 594 3.2. THEORIES OF BELIEF 595
3.3. SYNCHRONIZATION OF MULTI-AGENTS 595 4. DISTRIBUTED PLANNING SYSTEMS
596 4.1. ROUTE PLANNING USING DISTRIBUTED TECHNIQUES 596 4.2.
DISTRIBUTED NOAH 600 XX 5. DISTRIBUTED PLANNING SYNCHRONIZATION EXAMPLES
601 5.1. CSP INFLUENCED SYNCHRONIZATION METHOD 601 5.2. PARTIAL PLAN
SYNCHRONIZATION 605 5.3. LOGIC BASED PLAN SYNCHRONIZATION 606 6.
APPLICATION OF LEARNING TO PLANNING 608 7. CONCLUSIONS 610 REFERENCES
612 CHAPTER 21 KNOWLEDGE-BASED SUPERVISION OF FLEXIBLE MANUFACTURING
SYSTEMS A. K. A. TOGUYENI, E. CRAYE AND J.-C. GENTINA 1. SUPERVISION AND
AI-TECHNIQUES 631 2. PILOTING FUNCTIONS 632 2.1. INTRODUCTION 632 2.2.
PROBLEMS MET FROM DESIGN TO IMPLEMENTATION 633 2.3. THE KNOWLEDGE-BASED
SYSTEM 634 2.4. CONCLUSION 637 3. MANAGER OFWORKING MODES 637 3.1.
INTRODUCTION 637 3.2. REPRESENTATION AND MODELLING OF THE PROCESS 638
3.3. THE MANAGER FRAMEWORK 642 3.4. CONCLUSION 648 4. A MODEL-BASED
DIAGNOSTIC SYSTEM FOR ON-LINE MONITORING 650 4.1. INTRODUCTION 650 4.2.
THE MODELLING METHOD 650 4.3. THE CAUSAL TEMPORAL SIGNATURE OR CTS 651
4.4. THE MULTI-AGENT FRAMEWORK OF DIAGNOSTIC SYSTEM 655 4.5. CONCLUSION
660 5. GENERAL CONCLUSION 660 REFERENCES 661 XXI CHAPTER 22 A SURVEY OF
KNOWLEDGE-BASED INDUSTRIAL SCHEDULING K. S. HINDI AND M. G. SINGH 1.
INTRODUCTION 663 2. KNOWLEGDE ACQUISITION 664 3. KNOWLEDGE
REPRESENTATION 665 3.1. LOGIC-BASED SYSTEMS 665 3.2. RULE-BASED SYSTEMS
666 3.3. FRAME-BASED SYSTEMS 667 3.4. MULTI KNOWLEDGE REPRESENTATION
SYSTEMS 668 4. TEMPORAL ISSUES 669 5. CONTROL MECHANISMS 670 5.1.
FORWARD REASONING SYSTEMS 670 5.2. CONSTRAINT-DIRECTED AND OPPORTUNISTIC
SYSTEMS 671 5.3. MIXED CONTROL SYSTEMS 673 6. KNOWLEDGE BASED SCHEDULING
SYSTEMS (KBSS) 674 6.1. THE PRIMARY SCHEDULER (PS) 675 6.2. THE
HEURISTIC SCHEDULER (HS) 676 6.2. THE BACKTRACKING SCHEDULER (BS) 677 7.
REACTIVE AND REAL-TIME SCHEDULING 678 8. CONCLUSIONS 679 REFERENCES 680
CHAPTER 23 REACTIVE BATCH SCHEDULING V. J. TERPSTRA AND H. B. VERBRUGGEN
1. INTRODUCTION 688 L.L.PROJECT 688 1.2. SCHEDULING 688 1.3. EXAMPLE
CASE 689 1.4. DEFINITIONS 690 XX11 2. SCHEDULING STRATEGY ^NI 2.1.
MODELLING GO 2 2.2. MODULARITY ^92 2.3. PREDICTION AND CYCLES ^93 2.4.
REACTIVE BEHAVIOUR GEN 2.5. ROBUSTNESS Z-04 3. MODELLING 694 3.1.
THEEQUIPMENT MODEL ^95 3.2. THE MASTER REEIPE 697 3.3. MASTER SCHEDULE
^08 3.4. THE DEGREES OF FREEDOM OF THE SCHEDULER 699 4. PLANNER 699 5.
INTEGER SCHEDULER *Q 6. NON-INTEGER SCHEDULER 7 04 6.1. GANERATION OF
NLP MODEL 704 6.2. DEDICATED NLP SOLVER 70 7 7. REACTIVENESS 7 ^O 7.1.
HORIZONS 7^O 7.2. SAMPLE RATE 7^9 7.3. THREE CONTROL LOOPS IN SCHEDULER
. 709 7.4 ERROR SIGNAL 71 Q 7.5. TIMING 711 7.6. PROGRESSIVE REASONING
7^3 7.7. ANTICIPATORY SCHEDULES 714 7.8. PARALLELISM 71FI 8. ROBUSTNESS
ANALYSIS 71FI 9. IMPLEMENTATION AND RESULTS 719 10. CONCLUSIONS 7^^
REFERENCES 7 ~^ XXU1 CHAPTER 24 APPLYING GROUPWARE TECHNOLOGIES TO
SUPPORT MANAGEMENT IN ORGANIZATIONS A. MICHAILIDIS, P.-I. GOUMA AND R.
RADA 1. INTRODUCTION 723 2. GROUPWARE 723 2.1. GROUPS AND
COMPUTER-SUPPORTED COOPERATIVE WORK 724 2.2. GROUPWARE TAXONOMY 724
2.3.REVIEW OF GROUPWARE SYSTEMS 728 3. MANAGEMENT 729 3.1. ORGANIZATIONS
730 3.2. MANAGING ORGANIZATIONS 733 3.3. IT SYSTEMS FOR
MANAGEMENT-SUPPORT IN ORGANIZATIONS 735 3.4. COMPARING R&D DEPARTMENT
WITH ORGANIZATIONS 737 4. CASE STUDY 738 4.1. MODELLING THE
ORGANIZATIONAL STRUCTURE 739 4.2. THE ACTIVITY MODEL ENVIRONMENT (AME)
MODEL 739 4.3. THE MODIFIED VERSION OF AME 740 5. IMPLEMENTATION- THE
MUCH SYSTEM 745 6. CONCLUSION 747 REFERENCES 748 INDEX 757
|
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| discipline | Informatik Werkstoffwissenschaften / Fertigungstechnik Wirtschaftswissenschaften |
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| id | DE-604.BV010573865 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T17:55:16Z |
| institution | BVB |
| isbn | 0792333209 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007048704 |
| oclc_num | 31662287 |
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| owner | DE-473 DE-BY-UBG DE-521 DE-11 DE-525 DE-188 |
| owner_facet | DE-473 DE-BY-UBG DE-521 DE-11 DE-525 DE-188 |
| physical | XXIX, 767 S. graph. Darst. |
| publishDate | 1995 |
| publishDateSearch | 1995 |
| publishDateSort | 1995 |
| publisher | Kluwer |
| record_format | marc |
| series | International series on microprocessor-based and intelligent systems engineering |
| series2 | International series on microprocessor-based and intelligent systems engineering |
| spelling | Artificial intelligence in industrial decision making, control and automation ed. by Spyros G. Tzafestas ... Dordrecht [u.a.] Kluwer 1995 XXIX, 767 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier International series on microprocessor-based and intelligent systems engineering 14 Decision support systems gtt Industrie gtt Intelligence artificielle ram Kunstmatige intelligentie gtt Künstliche Intelligenz Decision support systems Intelligent control systems Automation Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Unternehmen (DE-588)4061963-1 s DE-604 Tzaphestas, Spyros G. 1939- Sonstige (DE-588)12055707X oth International series on microprocessor-based and intelligent systems engineering 14 (DE-604)BV023555365 14 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007048704&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Artificial intelligence in industrial decision making, control and automation International series on microprocessor-based and intelligent systems engineering Decision support systems gtt Industrie gtt Intelligence artificielle ram Kunstmatige intelligentie gtt Künstliche Intelligenz Decision support systems Intelligent control systems Automation Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd Unternehmen (DE-588)4061963-1 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4061963-1 |
| title | Artificial intelligence in industrial decision making, control and automation |
| title_auth | Artificial intelligence in industrial decision making, control and automation |
| title_exact_search | Artificial intelligence in industrial decision making, control and automation |
| title_full | Artificial intelligence in industrial decision making, control and automation ed. by Spyros G. Tzafestas ... |
| title_fullStr | Artificial intelligence in industrial decision making, control and automation ed. by Spyros G. Tzafestas ... |
| title_full_unstemmed | Artificial intelligence in industrial decision making, control and automation ed. by Spyros G. Tzafestas ... |
| title_short | Artificial intelligence in industrial decision making, control and automation |
| title_sort | artificial intelligence in industrial decision making control and automation |
| topic | Decision support systems gtt Industrie gtt Intelligence artificielle ram Kunstmatige intelligentie gtt Künstliche Intelligenz Decision support systems Intelligent control systems Automation Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd Unternehmen (DE-588)4061963-1 gnd |
| topic_facet | Decision support systems Industrie Intelligence artificielle Kunstmatige intelligentie Künstliche Intelligenz Intelligent control systems Automation Artificial intelligence Unternehmen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007048704&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV023555365 |
| work_keys_str_mv | AT tzaphestasspyrosg artificialintelligenceinindustrialdecisionmakingcontrolandautomation |