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Artificial Intelligence in Industrial Decision Making. Control and Automation
International Series on
MICROPROCESSOR-BASED AND INTELLIGENT SYSTEMS ENGINEERING
VOLUME 14
Editor
Professor S. G. Tzafestas, National Technical University, Athens, Greece
Editorial Advisory Board
Professor c. S. Chen, University of Akron, Ohio, U.S.A. Professor T. Fokuda, Nagoya University, Japan Professor F. Harashima, University of Tokyo, Tokyo, Japan Professor G. Schmidt, Technical University of Munich, Germany Professor N. K. Sinha, McMaster University, Hamilton, Ontario, Canada Professor D. Tabak, George Mason University, Fairfax, Virginia, U.S.A. Professor K. Valavanis, University of Southern Louisiana, Lafayette, u.S.A.
Artificial Intelligence in Industrial Decision Making,
Control and Automation
edited by
SPYROS G. TZAFESTAS Department of Electrical and Computer Engineering,
National Technical University oj Athens, Athens, Greece
and
HENKB.VERBRUGGEN Department of Electrical Engineering,
Delft University o/Technology, Delft. The Netherlands
SPRINGER-SClENCE+BUSINESS MEDIA, B.V.
Library of Congress Cataloging-in-Publication Data
Artificial intelligence in industrial decision making, control, and automation I edited b~ Spyros G. Tzafestas and Henk B. Verbruggen.
p. cm. -- (International series on microprocessor-based and intelligent systems engineering; v. 14)
Includes index. ISBN 978-94-010-4134-8 ISBN 978-94-011-0305-3 (eBook)
DOI 10.1007/978-94-011-0305-3 1. Decision support systems. 2. Intelligent control systems.
3. Automation. 4. Artificial intelligence. 1. Tzafestas, S. G., 1939- 11. Verbruggen, H. B. 111. Series. T58.62.A78 1995 658.4'03--dc20 94-46547
ISBN 978-94-010-4134-8
Printed on acid-free paper
All Rights Reserved © 1995 Springer Science+Business Media Dordrecht Originally published by Kluwer Academic Publishers in 1995 Softcover reprint of the hardcover 1 st edition 1995 No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, inc1uding photocopying, recording or by any information storage and retrieval system, without written permission from the copyright owner.
CONTENTS
Preface .......................................... .......................... .................. ......................... xxv
Contributors ....................................................................................................... xxvii
PART! GENERAL ISSUES
CHAPTER 1
ARTIFICIAL INTELLIGENCE IN INDUSTRIAL DECISION MAKING,
CONTROL AND AUTOMATION: AN INTRODUCTION
S. Tzafestas and H. Verbruggen
1. Introduction ......................................................................................................... 1
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 ..... : ............................................................................ 16
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 ....................................................................................................... 30
References ......................................................................................................... 31
vi
CHAPTER 2
CONCEPTUAL INTEGRATION OF QUALITATIVE AND
QUANTIT ATIVE 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 models ............................................................ 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. Individuals and individual conditions ........................................................ 52
4.2. Quantity conditions and preconditions ...................................................... 54
4.3. Relations .................................................................................................... 56
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 models ................................................................. 61
6. Discussion and Conclusion ................................................................................ 63
References ........................................................................................................ 64
CHAPTER 3
TIMING PROBLEMS AND THEIR HANDLING AT SYSTEM
INTEGRATION
L. MotDs
vii
1. Introduction ....................................................................................................... 67
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 ....................................................................................................... 85
References ........................................................................................................ 85
CHAPTER 4
ANAL YSIS FOR CORRECT REASONING IN INTERACTIVE MAN
ROBOT SYSTEMS: DISJUNCTIVE SYLLOGISM WITH MODUS
PONENS AND MODUS TOLLENS
E. C. Koenig
1. Introduction ....................................................................................................... 89
2. Valid Command Arguments .............................................................................. 90
viii
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
CHAPTERS
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 Buckling 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
MUL TIRESOLUTIONAL ARCHITECTURES FOR AUTONOMOUS
SYSTEMS WITH INCOMPLETE AND INADEQUATE KNOWLEDGE
REPRESENT ATION
A. Meystel
1. Introduction ..................................................................................................... 159
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: t-entropy ..................................................... 194
7. MCA in Autonomous Control System ............................................................ 195
7.1. The multiresolutional generalization of system models ........................... 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's optimality 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. Case Studies ................................................................................................... 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 ................................................................................................ 252
Xll
3.2. SOJA/SONIA .......................................................................................... 254
3.3. Y AMS ...................................................................................................... 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 models and ANN ........................................................................... 300
5. Networks .......................................................................................................... 302
5.1. Multilayered 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 definition ............................................................. 306
6.2. Model description for identification ....................................................... 308
7. Hybrid Modelling ............................................................................................ 308
Orthogonal least-squares algorithm.: .............................................................. 309
8. Model Validation ............................................................................................. 313
9. Experiments and Results Using Neural Identification .................................... 314
] O. Conclusions ................................................................................................... 323
References ..................................................................................................... 323
CHAPTER 11
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: DIM A 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) ................................................................ 378
6.3. Hopfield network ...................................................................................... 381
References ....................................................................................................... 384
CHAPTER 12
CONTROL STRATEGY OF ROBOTIC MANIPULATOR BASED ON
FLEXIBLE NEURAL NETWORK STRUCTURE
M. Teshnehlab and K. Watanabe
1. Introduction ..................................................................................................... 389
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 ............................................................ 396
4.2. Adaptation of neural network controller .................................................. 396
5. Simulation Example ........................................................................................ 397
6. Conclusion ....................................................................................................... 402
References ....................................................................................................... 402
CHAPTER 13
NEURO - FUZZY APPROACHES TO ANTICIPATORY CONTROL
L.H. Tsoukalas, A. Ikonomopoulos and R.E. Uhrig
1. Introduction ..................................................................................................... 405
2. Issues of Formalism Anticipatory Systems ..................................................... 407
3. Issues of Measurement and Prediction ............................................................ 412
4. Conclusions ..................................................................................................... 417
References ....................................................................................................... 418
xv
CHAPTER 14
NEW APPROACHES TO LARGE - SCALE SCHEDULING PROBLEMS:
CONSTRAINT DIRECTED PROGRAMMING AND NEURAL
NETWORKS
Y. Kobayashi and H. Nonaka
1. Introduction ..................................................................................................... 421
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 F AUL T 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. The D - 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
xvii
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 Anaiysis System (MIDAS) ................................ 512
3.1. MIDAS models ......................................................................................... 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
PARTS 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 ................................................................................................ 538
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 ...................................................................................................... 553
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: The minimal 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 of Working 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
CHAPTER 22
A SURVEY OF KNOWLEDGE-BASED INDUSTRIAL SCHEDULING
K. S. Hindi and M. G. Singh
xxi
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
1.1. Project ...................................................................................................... 688
1.2. Scheduling ............................................................................................... 688
1.3. Example case ........................................................................................... 689
1.4. Definitions ............................................................................................... 690
xxii
2. Scheduling strategy .......................................................................................... 691
2. 1. Modelling .................................................................................................. 692
2.2. Modularity ................................................................................................ 692
2.3. Prediction and cycles ................................................................................ 693
2.4. Reactive behaviour ................................................................................... 693
2.5. Robustness ................................................................................................ 694
3. Modelling ......................................................................................................... 694
3.1. The equipment model ............................................................................... 695
3.2. The master recipe ...................................................................................... 697
3.3. Master schedule ........................................................................................ 698
3.4. The degrees of freedom of the scheduler .................................................. 699
4. Planner ............................................................................................................. 699
5. Integer scheduler. ............................................................................................. 700
6. Non-integer scheduler. ..................................................................................... 704
6.1. Ganeration of NLP model. ........................................................................ 704
6.2. Dedicated NLP solver ............................................................................... 707
7. Reactiveness .................................................................................................... 708
7.1. Horizons ................................................................................................... 708
7.2. Sample Rate .............................................................................................. 709
7.3. Three Control Loops in Scheduler ............................................................ 709
7.4 Error Signal. ............................................................................................... 710
7.5. Timing ...................................................................................................... 711
7.6. Progressive Reasoning .............................................................................. 713
7.7. Anticipatory Schedules ............................................................................. 714
7.8. Parallelism ................................................................................................ 716
8. Robustness analysis ......................................................................................... 716
9. Implen1entation and Results ............................................................................ 719
10. Conclusions .................................................................................................. 720
References ....................................................................................................... 720
CHAPTER 24
APPLYING GROUPWARE TECHNOLOGIES TO SUPPORT
MANAGEMENT IN ORGANIZATIONS
A. Michailidis, P.-I. Gouma and R. Rada
xxiii
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
PREFACE
This book is concerned with Artificial Intelligence (AI) concepts and techniques as
applied to industrial decision making, control and automation problems. The field of AI
has been expanded enormously during the last years due to that solid theoretical and
application results have accumulated. During the first stage of AI development most
workers in the field were content with illustrations showing ideas at work on simple
problems. Later, as the field matured, emphasis was turned to demonstrations that
showed the capability of AI techniques to handle problems of practical value. Now, we
arrived at the stage where researchers and practitioners are actually building AI systems
that face real-world and industrial problems.
This volume provides a set of twenty four well-selected contributions that deal
with the application of AI to such real-life and industrial problems. These contributions
are grouped and presented in five parts as follows:
Part 1: General Issues
Part 2: Intelligent Systems
Part 3: Neural Networks in Modelling, Control and Scheduling
Part 4: System Diagnostics
Part 5: Industrial Robotic, Manufacturing and Organizational Systems
Part 1 involves four chapters providing background material and dealing with
general issues such as the conceptual integration of qualitative and quantitative models,
the treatment of timing problems at system integration, and the investigation of correct
reasoning in interactive man-robot systems.
Part 2 presents a number of systems with built-in intelligence. It starts with an
introduction to the concept of intelligent control systems and continues with the
demonstration of an autonomous control synthesis system (called phase space
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navigator) for nonlinear control systems. Then, an overview of the hierarchical and
behavioural approaches to autonomous robotic systems is provided, and a combined
(behavioural plus planning) approach is developed which possesses a multiresolutional
hierarchy of behaviours. Then, a study on distributed intelligent systems in robotics is
provided, which is based on an intelligent cellular robotic system (CEBOT) that
consists of a number of autonomous robotic units called cells. This part finishes with
a contribution showing that subtasks of manufacturing control are so complex and
interconnected that cannot be modelled by a single agent system. The problem solution
can be achieved using only intensive goal oriented cooperation with other experts.
Part 3 is devoted to artificial neural networks (ANN). First, the application of
ANNs to systems modelling and identification is examined including some
experimental results. Then, the application of ANNs to robot control is reviewed. The
basic architectures of neural control are described, and several illustrative robotic
exampes are included. Then, a robotic neurocontroller is described which makes use
of bipolar neurons to learn the inverse model of the system. The backpropagation
algorithm is used to learn the inverse dynamic model, and the feedback-error-Iearning
scheme is employed as a learning method for the feedforward controller. A 2-link
robotic example is included. Next, the neuro-fuzzy approach to anticipatory control is
considered. Anticipatory systems can utilize fuzzy predictions about the future in
regulating their behaviour through "virtual measurement" which is mapped using
ANNs. Finally, the class of large-scale scheduling problems is investigated through
interactive and automated approaches. The ANN here is used to treat the combinatorial
optimization problems resulting from the scheduling problems.
Part 4 contains three contributions on fault diagnosis. The first contribution
provides an overview on the knowledge-based approach to the fault diagnosis of
technological systems. First-generation and second-generation diagnostic expert
systems are discussed, a survey of digital systems diagnostics tools is presented, and
two general methodologies for the development of fault diagnostic tools are
developed. The second, presents and compares two methods for model-based
diagnosis, namely the diagnostic model processor (DMP) and the model integrated
diagnosis analysis system (MIDAS). Finally, the third contribution describes the
multilevel flow model (MFM) which belongs to the class of means-end models. The
basic ideas of MFM are outlined, and three diagnostic reasoning methods which can
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be efficiently implemented with the aid of MFM are developed. These methods have
been implemented on the G2 programming tool.
Part 5 involves a number of useful applications of AI. The first contribution is
concerned with the problem of multisensor integration for mobile robot navigation. In
particular, a mobile platform navigating in a 2D environment with unknown obstacles
is considered. The second, is concerned with the use of reactive robots for incremental
design of flexible robotic assembly cells. A layered reactive architecture for assembly
robots that possesses robustness, reactivity and incrementality is proposed, and a
series of simulation experiments are described. The next contribution provides a
comparative review of AI and DAI (distributed AI) based planning techniques for
manufacturing systems. Planning is a central function in all automated systems, and
consists in the selection of the sequence of compatible tasks/actions by which the
system goals are achieved. This part continues with a contribution on knowledge-based
supervision of flexible manufacturing systems (FMS), and a survey of knowledge
based techniques for industrial scheduling. Supervision of FMSs covers different
kinds of activities such as the piloting, the management of working models and the
monitoring of the failures. Knowledge-based techniques, in contrast to operational
research techniques, are suitable for generating on-line dynamic schedules based on the
actual system state. Then a contribution on reactive on-line batch scheduling is
presented. A design method for a robust on line scheduler is provided that makes a
prediction of the effects of the schedule and tries to optimize the global plant
performance. This scheduler is composed by a planner, an integer scheduler and a
non-integer scheduler, and was implemented on the real-time expert system shell G2.
Finally, a contribution is given on technological support which becomes a "must" in
modern organizations and extends the area of management. Here the groupware
technology is adopted, which can provide the kind of support the manager needs to
deal with uncertainty and ambiguity, and a tool is developed that can supervise and
coordinate the overall use of the system and mediate the interactions among its users.
Taken together the contributions of the book provide a well balanced and
representative picture of the capabilities of current AI techniques to treat important
decision-making and control problems in real-scale robotic, manufacturing and other
industrial systems. These techniques which are in the center of the computer revolution
relieve us of a great deal of a mental effort, in the same way that the techniques of the
industrial revolution relieve us of a great deal of physical labour. The results on the
xxviii
actual applications of AI are widely sparse in the literature, and only a few books of a
nature similar to the present book exist on the subject. Thus the editors feel that this
book provides an important addition, since it presents in collective form several angles
of attack, methodologies and applications. Each chapter is self-contained, and in many
cases includes review material and how-to-do issues.
The book is suitable for the researcher and practitioner of the field, as well as for
the educator and senior graduate student. The editors are indebted to all contributors for
their high quality contributions, and to Kluwer's (Dordrecht) editorial staff members
for their particular care throughout the editorial and printing process.
Spyros G. Tzafestas
Henk B. Verbruggen
ALBA YARAK S. ARAIJOH. ARZEN K.-E. BRACKNEYL. BRUIJN P. M. CRAYEE. DIAS J. FUKUDAT. GENTINA J.-C. GOUMA P.-I. HINDI K.S. IKONOMOPOULOS A KOBA Y ASH! Y. KOENIG E.C. KOKKINAKI AI. KRALLMANN H. KRIJGSMAN AJ. LARSSON J.E. MEYSTELA. MICHAILIDIS A MOruSL. NONAKAH. NUNES U. PETTIT. RADAR. SEKIYAMAK. SHOURESHI R. SINGH M.G. TERPSTRA V.J. TESHNEHLAB M. TOGUYENI AK.A TRACA de ALMEIDA A TSOUKALAS L. TZAFESTAS E.S. TZAFESTAS S.G. UEYAMA T. UHRIG R.E. V ALA V ANIS K.P. VERBRUGGEN H.B. WATANABEK. WALLEN A WHEELERM. WOODSE.A ZHAOF.
CONTRIBUTORS
T.U. Berlin, Berlin, Germany Univ. of Coimbra, Coimbra, Portugal Lund Inst. of Technology, Lund, Sweden Purdue Univ. West Lafayette, U.S.A Delft Univ. of Technology, Delft, The Netherlands Ecole Centrale de Lille, Lille, France Univ. of Coimbra, Coimbra, Portugal Nagoya Univ., Nagoya, Japan Ecole Centrale de Lille, Lille, France Univ. of Liverpool, Liverpool, U.K. Dept. of Computation, UMIST, Manchester, U.K. The Univ. of Tennessee, Knoxville, U.S.A Energy Res. Lab., Hitachi Ltd., Ibaraki-ken, Japan CS Dept., Univ. of Wisconsin-Madison, U.S.A Univ. of Southwestern Louisiana, Lafayette, U.S.A T.U. Berlin, Berlin, Germany Delft Univ. of Technology, Delft, The Netherlands Lund Inst. of Technology, Lund, Sweden Drexel Univ., Philadelphia, U.S.A Univ. of Liverpool, Liverpool, U.K. Tallinn Techn. Univ., Tallinn, Estonia Energy Res. Lab., Hitachi Ltd., Ibaraki-ken, Japan Univ. of Coimbra, Coimbra, Portugal Washington Res. Center, Columbia, MD, U.S.A Univ. of Liverpool, Liverpool, U.K. Nagoya Univ., Nagoya, Japan Purdue Univ., West Lafayette, U.S.A Dept. of Computation, UMIST, Manchester, U.K. Delft Univ. of Technology, Delft, The Netherlands Saga Univ., Graduate School, Japan Ecole Centrale de Lille, Lille, France Univ. of Coimbra, Coimbra, Portugal The Univ. of Tennessee, Knoxville, U.S.A Univ. P. et M. Curie, Paris, France Natl. Tech. Univ. of Athens, Athens, Greece Nagoya Univ., Nagoya, Japan The Univ. of Tennessee, Knoxville, U.S.A Univ. of Southwestern Louisiana, Lafayette, U.S.A Delft Univ. of Technology, Delft, The Netherlands Saga Univ., Mech. Eng. Dept., Saga, Japan Lund Inst. of Technology, Lund, Sweden Purdue Univ., West Lafayette, U.S.A SINTEF Automatic Control, Trondheim, Norway CIS Dept., Ohio State Univ., Ohio, U.S.A
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