Automated Planning: Theory & Practice

Ghallab, Malik; Nau, Dana; Traverso, Paolo

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Table of contents
  • Cover
  • Copyright Pageiv
  • Contentsvii
  • About the Authorsii
  • Forewordxxi
  • Prefacexxiii
  • Table of Notationxxvii
  • Chapter 1. Introduction and Overview1
  • 1.1 First Intuitions on Planning1
  • 1.2 Forms of Planning2
  • 1.3 Domain-Independent Planning3
  • 1.4 Conceptual Model for Planning5
  • 1.5 Restricted Model9
  • 1.6 Extended Models11
  • 1.7 A Running Example: Dock-Worker Robots13
  • Part I: Classical Planning17
  • Chapter 2. Representations for Classical Planning19
  • 2.1 Introduction19
  • 2.2 Set-Theoretic Representation20
  • 2.3 Classical Representation27
  • 2.4 Extending the Classical Representation34
  • 2.5 State-Variable Representation41
  • 2.6 Comparisons47
  • 2.7 Discussion and Historical Remarks49
  • 2.8 Exercises50
  • Chapter 3. Complexity of Classical Planning55
  • 3.1 Introduction55
  • 3.2 Preliminaries56
  • 3.3 Decidability and Undecidability Results57
  • 3.4 Complexity Results59
  • 3.5 Limitations65
  • 3.6 Discussion and Historical Remarks66
  • 3.7 Exercises66
  • Chapter 4. State-Space Planning69
  • 4.1 Introduction69
  • 4.2 Forward Search69
  • 4.3 Backward Search73
  • 4.4 The STRIPS Algorithm76
  • 4.5 Domain-Specific State-Space Planning78
  • 4.6 Discussion and Historical Remarks81
  • 4.7 Exercises81
  • Chapter 5. Plan-Space Planning85
  • 5.1 Introduction85
  • 5.2 The Search Space of Partial Plans86
  • 5.3 Solution Plans91
  • 5.4 Algorithms for Plan-Space Planning94
  • 5.5 Extensions100
  • 5.6 Plan-Space versus State-Space Planning101
  • 5.7 Discussion and Historical Remarks103
  • 5.8 Exercises105
  • Part II: Neoclassical Planning111
  • Chapter 6. Planning-Graph Techniques113
  • 6.1 Introduction113
  • 6.2 Planning Graphs114
  • 6.3 The Graphplan Planner123
  • 6.4 Extensions and Improvements of Graphplan131
  • 6.5 Discussion and Historical Remarks137
  • 6.6 Exercises139
  • Chapter 7. Propositional Satisfiability Techniques143
  • 7.1 Introduction143
  • 7.2 Planning Problems as Satisfiability Problems144
  • 7.3 Planning by Satisfiability151
  • 7.4 Different Encodings160
  • 7.5 Discussion and Historical Remarks164
  • 7.6 Exercises165
  • Chapter 8. Constraint Satisfaction Techniques167
  • 8.1 Introduction167
  • 8.2 Constraint Satisfaction Problems168
  • 8.3 Planning Problems as CSPs172
  • 8.4 CSP Techniques and Algorithms178
  • 8.5 Extended CSP Models185
  • 8.6 CSP Techniques in Planning187
  • 8.7 Discussion and Historical Remarks189
  • 8.8 Exercises190
  • Part III: Heuristics and Control Strategies193
  • Chapter 9. Heuristics in Planning199
  • 9.1 Introduction199
  • 9.2 Design Principle for Heuristics: Relaxation199
  • 9.3 Heuristics for State-Space Planning201
  • 9.4 Heuristics for Plan-Space Planning208
  • 9.5 Discussion and Historical Remarks213
  • 9.6 Exercises214
  • Chapter 10. Control Rules in Planning217
  • 10.1 Introduction217
  • 10.2 Simple Temporal Logic218
  • 10.3 Progression220
  • 10.4 Planning Procedure222
  • 10.5 Extensions223
  • 10.6 Extended Goals224
  • 10.7 Discussion and Historical Remarks226
  • 10.8 Exercises227
  • Chapter 11. Hierarchical Task Network Planning229
  • 11.1 Introduction229
  • 11.2 STN Planning231
  • 11.3 Total-Order STN Planning238
  • 11.4 Partial-Order STN Planning240
  • 11.5 HTN Planning244
  • 11.6 Comparisons250
  • 11.7 Extensions252
  • 11.8 Extended Goals256
  • 11.9 Discussion and Historical Remarks257
  • 11.10 Exercises259
  • Chapter 12. Control Strategies in Deductive Planning263
  • 12.1 Introduction263
  • 12.2 Situation Calculus264
  • 12.3 Dynamic Logic270
  • 12.4 Discussion and Historical Remarks276
  • 12.5 Exercises278
  • Part IV: Planning with Time and Resources281
  • Chapter 13. Time for Planning285
  • 13.1 Introduction285
  • 13.2 Temporal References and Relations285
  • 13.3 Qualitative Temporal Relations290
  • 13.4 Quantitative Temporal Constraints302
  • 13.5 Discussion and Historical Remarks306
  • 13.6 Exercises307
  • Chapter 14. Temporal Planning309
  • 14.1 Introduction309
  • 14.2 Planning with Temporal Operators310
  • 14.3 Planning with Chronicles326
  • 14.4 Discussion and Historical Remarks343
  • 14.5 Exercises345
  • Chapter 15. Planning and Resource Scheduling349
  • 15.1 Introduction349
  • 15.2 Elements of Scheduling Problems351
  • 15.3 Machine Scheduling Problems356
  • 15.4 Integrating Planning and Scheduling362
  • 15.5 Discussion and Historical Remarks372
  • 15.6 Exercises373
  • Part V: Planning under Uncertainty375
  • Chapter 16. Planning Based on Markov Decision Processes379
  • 16.1 Introduction379
  • 16.2 Planning in Fully Observable Domains380
  • 16.3 Planning under Partial Observability392
  • 16.4 Reachability and Extended Goals397
  • 16.5 Discussion and Historical Remarks398
  • 16.6 Exercises400
  • Chapter 17. Planning Based on Model Checking403
  • 17.1 Introduction403
  • 17.2 Planning for Reachability Goals404
  • 17.3 Planning for Extended Goals414
  • 17.4 Planning under Partial Observability425
  • 17.5 Planning as Model Checking versus MDPs432
  • 17.6 Discussion and Historical Remarks432
  • 17.7 Exercises434
  • Chapter 18. Uncertainty with Neoclassical Techniques437
  • 18.1 Introduction437
  • 18.2 Planning as Satisfiability437
  • 18.3 Planning Graphs443
  • 18.4 Discussion and Historical Remarks446
  • 18.5 Exercises447
  • Part VI: Case Studies and Applications449
  • Chapter 19. Space Applications451
  • 19.1 Introduction451
  • 19.2 Deep Space 1451
  • 19.3 The Autonomous Remote Agent451
  • 19.4 The Remote Agent Architecture453
  • 19.5 The Planner Architecture457
  • 19.6 The Deep Space 1 Experiment461
  • 19.7 Discussion and Historical Remarks466
  • Chapter 20. Planning in Robotics469
  • 20.1 Introduction469
  • 20.2 Path and Motion Planning471
  • 20.3 Planning for the Design of a Robust Controller477
  • 20.4 Dock-Worker Robots487
  • 20.5 Discussion and Historical Remarks490
  • Chapter 21. Planning for Manufacturability Analysis493
  • 21.1 Introduction493
  • 21.2 Machined Parts493
  • 21.3 Feature Extraction495
  • 21.4 Generating Abstract Plans497
  • 21.5 Resolving Goal Interactions499
  • 21.6 Additional Steps499
  • 21.7 Operation Plan Evaluation502
  • 21.8 Efficiency Considerations502
  • 21.9 Concluding Remarks503
  • Chapter 22. Emergency Evacuation Planning505
  • 22.1 Introduction505
  • 22.2 Evacuation Operations506
  • 22.3 Knowledge Representation507
  • 22.4 Hierarchical Task Editor508
  • 22.5 SiN509
  • 22.6 Example512
  • 22.7 Summary513
  • 22.8 Discussion and Historical Remarks515
  • Chapter 23. Planning in the Game of Bridge517
  • 23.1 Introduction517
  • 23.2 Overview of Bridge517
  • 23.3 Game-Tree Search in Bridge519
  • 23.4 Adapting HTN Planning for Bridge521
  • 23.5 Implementation and Results524
  • Part VII: Conclusion525
  • Chapter 24. Other Approaches to Planning527
  • 24.1 Case-Based Planning527
  • 24.2 Linear and Integer Programming529
  • 24.3 Multiagent Planning530
  • 24.4 Plan Merging and Plan Rewriting531
  • 24.5 Abstraction Hierarchies532
  • 24.6 Domain Analysis534
  • 24.7 Planning and Learning535
  • 24.8 Planning and Acting, Situated Planning, and Dynamic Planning536
  • 24.9 Plan Recognition537
  • 24.10 Suggestions for Future Work540
  • Part VIII: Appendices541
  • Appendix A. Search Procedures and Computational Complexity543
  • A.1 Nondeterministic Problem Solving543
  • A.2 State-Space Search544
  • A.3 Problem-Reduction Search548
  • A.4 Computational Complexity of Procedures550
  • A.5 Computational Complexity of Problems551
  • A.6 Planning Domains as Language-Recognition Problems552
  • A.7 Discussion and Historical Remarks553
  • Appendix B. First-Order Logic555
  • B.1 Introduction555
  • B.2 Propositional Logic555
  • B.3 First-Order Logic557
  • Appendix C. Model Checking561
  • C.1 Introduction561
  • C.2 Intuitions561
  • C.3 The Model Checking Problem563
  • C.4 Model Checking Algorithms565
  • C.5 Symbolic Model Checking567
  • C.6 BDD-Based Symbolic Model Checking570
  • Bibliography573
  • Index609
Book details
  • Vendor Elsevier S & T
  • SKU 9781558608566R150
  • ISBN-13 9780080490519
  • Author Ghallab, Malik; Nau, Dana; Traverso, Paolo
  • Category Technology & Engineering
  • Subject Robotics

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Automated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications.

Automated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well beyond classical planning, to include temporal planning, resource scheduling, planning under uncertainty, and modern techniques for plan generation, such as task decomposition, propositional satisfiability, constraint satisfaction, and model checking.

The authors combine over 30 years experience in planning research and development to offer an invaluable text to researchers, professionals, and graduate students.

*Comprehensively explains paradigms for automated planning.
*Provides a thorough understanding of theory and planning practice, and how they relate to each other.
*Presents case studies of applications in space, robotics, CAD/CAM, process control, emergency operations, and games.

*Provides a thorough understanding of AI planning theory and practice, and how they relate to each other.
*Covers all the contemporary topics of planning, as well as important practical applications of planning, such as model checking and game playing.
*Presents case studies and applications in planning engineering, space, robotics, CAD/CAM, process control, emergency operations, and games.
*Provides lecture notes, examples of programming assignments, pointers to downloadable planning systems and related information online.