LEADER 05609nam 22007094a 450 001 9910827091803321 005 20200520144314.0 010 $a1-281-00721-8 010 $a9786611007218 010 $a0-08-049051-4 024 8 $a(WaSeSS)ssj0000107939 035 $a(CKB)1000000000016240 035 $a(EBL)333985 035 $a(OCoLC)156908421 035 $a(SSID)ssj0000107939 035 $a(PQKBManifestationID)11135221 035 $a(PQKBTitleCode)TC0000107939 035 $a(PQKBWorkID)10016023 035 $a(PQKB)11045633 035 $a(Au-PeEL)EBL333985 035 $a(CaPaEBR)ebr10226616 035 $a(CaONFJC)MIL100721 035 $a(OCoLC-P)156908421 035 $a(CaSebORM)9781558608566 035 $a(MiAaPQ)EBC333985 035 $a(EXLCZ)991000000000016240 100 $a20031223d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAutomated planning $etheory and practice /$fGhallab Malik, Dana Nau, Paolo Traverso 205 $a1st ed. 210 $aAmsterdam ;$aBoston $cElsevier/Morgan Kaufmann$dc2004 215 $a1 online resource (664 p.) 225 1 $aThe Morgan Kaufmann Series in Artificial Intelligence 300 $aDescription based upon print version of record. 311 $a1-4933-0370-8 311 $a1-55860-856-7 320 $aIncludes bibliographical references (p. 573-607) and index. 327 $aFront Cover; Automated Planning Theory and Practice; Copyright Page; Contents; About the Authors; Foreword; Preface; Table of Notation; Chapter 1. Introduction and Overview; 1.1 First Intuitions on Planning; 1.2 Forms of Planning; 1.3 Domain-Independent Planning; 1.4 Conceptual Model for Planning; 1.5 Restricted Model; 1.6 Extended Models; 1.7 A Running Example: Dock-Worker Robots; Part I: Classical Planning; Chapter 2. Representations for Classical Planning; 2.1 Introduction; 2.2 Set-Theoretic Representation; 2.3 Classical Representation; 2.4 Extending the Classical Representation 327 $a2.5 State-Variable Representation2.6 Comparisons; 2.7 Discussion and Historical Remarks; 2.8 Exercises; Chapter 3. Complexity of Classical Planning; 3.1 Introduction; 3.2 Preliminaries; 3.3 Decidability and Undecidability Results; 3.4 Complexity Results; 3.5 Limitations; 3.6 Discussion and Historical Remarks; 3.7 Exercises; Chapter 4. State-Space Planning; 4.1 Introduction; 4.2 Forward Search; 4.3 Backward Search; 4.4 The STRIPS Algorithm; 4.5 Domain-Specific State-Space Planning; 4.6 Discussion and Historical Remarks; 4.7 Exercises; Chapter 5. Plan-Space Planning; 5.1 Introduction 327 $a5.2 The Search Space of Partial Plans5.3 Solution Plans; 5.4 Algorithms for Plan-Space Planning; 5.5 Extensions; 5.6 Plan-Space versus State-Space Planning; 5.7 Discussion and Historical Remarks; 5.8 Exercises; Part II: Neoclassical Planning; Chapter 6. Planning-Graph Techniques; 6.1 Introduction; 6.2 Planning Graphs; 6.3 The Graphplan Planner; 6.4 Extensions and Improvements of Graphplan; 6.5 Discussion and Historical Remarks; 6.6 Exercises; Chapter 7. Propositional Satisfiability Techniques; 7.1 Introduction; 7.2 Planning Problems as Satisfiability Problems; 7.3 Planning by Satisfiability 327 $a7.4 Different Encodings7.5 Discussion and Historical Remarks; 7.6 Exercises; Chapter 8. Constraint Satisfaction Techniques; 8.1 Introduction; 8.2 Constraint Satisfaction Problems; 8.3 Planning Problems as CSPs; 8.4 CSP Techniques and Algorithms; 8.5 Extended CSP Models; 8.6 CSP Techniques in Planning; 8.7 Discussion and Historical Remarks; 8.8 Exercises; Part III: Heuristics and Control Strategies; Chapter 9. Heuristics in Planning; 9.1 Introduction; 9.2 Design Principle for Heuristics: Relaxation; 9.3 Heuristics for State-Space Planning; 9.4 Heuristics for Plan-Space Planning 327 $a9.5 Discussion and Historical Remarks9.6 Exercises; Chapter 10. Control Rules in Planning; 10.1 Introduction; 10.2 Simple Temporal Logic; 10.3 Progression; 10.4 Planning Procedure; 10.5 Extensions; 10.6 Extended Goals; 10.7 Discussion and Historical Remarks; 10.8 Exercises; Chapter 11. Hierarchical Task Network Planning; 11.1 Introduction; 11.2 STN Planning; 11.3 Total-Order STN Planning; 11.4 Partial-Order STN Planning; 11.5 HTN Planning; 11.6 Comparisons; 11.7 Extensions; 11.8 Extended Goals; 11.9 Discussion and Historical Remarks; 11.10 Exercises 327 $aChapter 12. Control Strategies in Deductive Planning 330 $aAutomated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications. Automated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well b 410 4$aThe Morgan Kaufmann Series in Artificial Intelligence 606 $aProduction planning$xData processing 615 0$aProduction planning$xData processing. 676 $a658.5 700 $aGhallab$b Malik$0289496 701 $aNau$b Dana S$01132634 701 $aTraverso$b Paolo$01626991 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910827091803321 996 $aAutomated planning$93963341 997 $aUNINA