Distributed Artificial Intelligence (eBook)

Volume II

Robin Gasser, Michael N. Huhns (Autoren)

eBook Download: PDF

2014 | 1. Auflage
649 Seiten
Elsevier Science (Verlag)
978-1-4832-9481-0 (ISBN)

Infos

Distributed Artificial Intelligence

Robin B. Gasser is Professor in Parasitology at the Faculty of Veterinary Science, University of Melbourne, Australia

Inhalt

Front Cover 
1 
Distributed Artificial Intelligence 2
Copyright Page 3
Table of Contents 4
Preface 6
Themes in Distributed Artificial Intelligence Research 8
Part I: Societies of Agents 18
Chapter 1. Cooperating Agents: A Unified Theory of Communication and Social Structure 20
Abstract 20
1.1 Introduction 20
1.2 Problems of Cooperation 21
1.3 Previous Approaches to the Communication Problem 22
1.4 Information Required for Cooperation 25
1.5 Two Types of Cooperation 26
1.6 A Theory of Cognitive States 28
1.7 Communication Theory 32
1.8 Social Cooperation and Communication 36
1.9 Social Roles and Structures 37
1.10 Social Structures and Social Groups 41
1.11 The Contract Net as a Social Group 44
1.12 An Example Language Game 47
1.13 Further Applications to Distributed Systems 48
1.14 Conclusion 49
Acknowledgements 50
References 50
Chapter 2. The Structure of 111-Structured Solutions: Boundary Objects and Heterogeneous Distributed Problem Solving 54
Abstract 54
2.1 Introduction: Larger than Life and Twice as Natural 55
2.2 From the Turing Test to the Durkheim Test 55
2.3 Due Process, the Frame Problem, and Scientific Communities 59
2.5 Heterogeneous Problem Solving and Boundary Objects 63
2.6 Types of Boundary Objects 64
2.7 Summary and Conclusions 68
Acknowledgements 69
References 69
Chapter 3. Representing and Using Organizational Knowledge in Distributed AI Systems 72
Abstract 72
3.1 Introduction 72
3.2 Viewing Coordination Frameworks as Patterns of Settled and Unsettled Problems 74
3.3 A Multiagent Problem: ICE 77
3·4 Settlement and Unsettlement 84
3.5 Problem Shifts and Organizational Change in ICE 85
3.6 Conclusions 92
Acknowledgements 93
References 93
Chapter 4. Dynamics of Computational Ecosystems: Implications for DAI 96
Abstract 96
4.1 Introduction 96
4.2 Model of Computational Ecosystems 97
4·3 Dynamical Behavior of Computational Ecosystems 99
4.4 Conclusion 110
References 111
Part II: Cooperation by Negotiation 114
Chapter 5. Communication-Free Interactions among Rational Agents: A Probabilistic Approach 116
Abstract 116
5.1 Introduction 117
5.2 Notation 120
5.3 Dominance 122
5.4 Rational Moves—A Prescription for an Agent 126
5.5 Axioms of Rationality—Description 129
5.6 The Jointly Prescriptive Issues 133
5.7 Conclusion 133
References 134
Chapter 6. Multiagent Compromise via Negotiation 136
Abstract 136
6.1 Introduction 136
6.2 Requirements for a Negotiation Planner 139
6.3 Negotiation Methods 141
6·4 Belief Modification through Persuasive Argumentation 145
6.5 Narrowing the Parties' Differences 149
6.6 Results 150
6.7 Conclusion 151
Acknowledgements 152
References 152
Chapter 7. Conflict-resolution Strategies for Nonhierarchical Distributed Agents 156
Abstract 156
7.1 Introduction 156
7.2 The Network Management Problem 157
7.3 Example 160
7.4 Conflict-resolution Paradigms 163
7.5 Experimental Plan 167
7.6 Experience and Results 169
7.7 Planning Requirements and the Structure of Agent Knowledge 171
7.8 Future Work 174
References 178
Chapter 8. Constraint-Directed Negotiation of Resource Reallocations 180
Abstract 180
8.1 Introduction 181
8.2 Approach 184
8.3 Representation 185
8.4 Negotiation Operators 190
8.5 The Negotiation Process and Experimental Results 193
8.6 Conclusions 208
References 208
Part III: Cooperation by Planning 212
Chapter 9. Plans for Multiple Agents 214
Abstract 214
9.1 Introduction 214
9.2 The Underlying Domain, Its Representation, and Execution in Parallel 216
9.3 Plan Verification 221
9.4 Plan Generation 226
9.5 Plan Execution 239
9.6 Conclusion 243
Acknowledgements 244
References 244
Chapter 10. Negotiating Task Decomposition and Allocation Using Partial Global Planning 246
Abstract 246
10.1 Introduction 246
10.2 Partial Global Planning 248
10.3 Implementation 249
10.4 Negotiation and Task Passing 251
10.5 Results 255
10.6 Discussion 258
Acknowledgements 259
References 259
Chapter 11. Mechanisms for Assessing Nonlocal Impact of Local Decisions in Distributed Planning 262
Abstract 262
11.1 Introduction 262
11.2 Reasoning about Constraints and Conflicts 264
11.3 Reasoning about Nonlocal Conflict 269
11.4 Status and Concluding Remarks 274
Acknowledgements 274
References 275
Chapter 12. An Object-oriented Multiple Agent Planning System 276
Abstract 276
12.1 Introduction 277
12.2 Related Work 278
12.3 An Object-oriented Approach to Multiagent Planning 280
12.4 System Structure 280
12.5 Control Structure 293
12.6 Example 294
12.7 Conclusions 303
Acknowledgements 305
References 305
Part IV: Architectures for DAI 308
Chapter 13. DATMS: A Framework for Distributed Assumption Based Reasoning 310
Abstract 310
13.1 Introduction 310
13.2 Background 311
13.3 Problem Solving Model 314
13.4 Issues 317
13.5 DATMS 319
13.6 Implementation Experiences with DATMS 330
13.7 Summary 333
Acknowledgements 333
References 333
Chapter 14. Experiments on Cage and Poligon: Measuring the Performance of Parallel Blackboard Systems 336
Abstract 336
14.1 Introduction 336
14.2 Background 337
14.3 The Advanced Architectures Project 345
14.4 Extending the Serial System: Cage 346
14.5 Pursuing a Daemon-driven Blackboard System: Poligon 354
14.6 The CARE Simulation System and Machine Architecture 362
14.7 The Elint Application 364
14.8 Experiments and Results 367
14.9 Discussion 394
14.10 Conclusions 396
Acknowledgements 398
References 398
Chapter 15. Distributing Intelligence within an Individual 402
Abstract 402
15.1 A Metaphor for DAI: The Intelligent Individual 402
15.2 Guardian's Task: Monitoring SICU Patients 404
15.3 Architecture for an Intelligent Individual 408
15.4 Guardian's Performance on a Typical SICU Scenario 416
15.5 Conclusions 424
Acknowledgements 426
References 426
Chapter 16. Learning and Adaptation In Distributed Artificial Intelligence Systems 430
Abstract 430
16.1 Introduction 430
16.2 Learning in AI Systems 432
16.3 A Framework for Incorporating Learning in DAI Systems 434
16.4 The Bidding Process 439
16.5 Adaptation through Genetic Transformation 440
16.6 Conclusions 443
References 444
Part V: Applications for DAI 448
Chapter 17. A Distributed Problem Solving Architecture for Knowledge Based Vision 450
Abstract 450
17.1 Introduction 451
17.2 Distributing the Knowledge Based Vision Pyramid 452
17.3 The Rational Cell 454
17.4 Cell Organization for Knowledge Based Vision 457
17.5 Application to Sonar Interpretation 467
17.6 Other Robotic Applications 472
17.7 Conclusions 472
17.8 Future Work 473
Acknowledgements 476
References 477
Chapter 18. The Cooperation of Experts in Engineering Design 480
Abstract 480
18.1 Introduction 481
18.2 Communication for Collaboration 483
18.3 A Model of Collaboration 486
18.4 Design and Operational Issues 494
18.5 Summary and Conclusion 497
Acknowledgements 498
References 499
Chapter 19. Evaluating Research in Cooperative Distributed Problem Solving 502
Abstract 502
19.1 Introduction 503
19.2 Goal: Limit Domain and Environmental Assumptions 504
19.3 Goal: Discover Paradigms for Building Cooperating Agents 507
19.4 Goal: Develop Methods for Assuring Global Coherence 509
19·5 Goal: Theories of Organizational Behavior and Control 511
19.6 Goal: Guaranteed Responsiveness and Fault Tolerance 514
19.7 Goal: Effective CDPS Communications Protocols 516
19.8 Goal: Sophisticated Agents 517
19.9 Goal: System and Hardware Support 519
19.10 Goal: Develop general and representative hard domain problems 520
19.11 Example CDPS Systems 522
19.12 Comparisons 527
19.13 Conclusions 535
Acknowledgements 535
References 536

Produkt-Details

• Erscheint lt. Verlag: 23.5.2014
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 1-4832-9481-1 / 1483294811
• ISBN-13: 978-1-4832-9481-0 / 9781483294810