Representation and parallelization techniques for classical planning:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
2012
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| Schlagworte: | |
| Online-Zugang: | Volltext https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20120404-1083789-1-4 Inhaltsverzeichnis |
| Beschreibung: | XVII, 181 S. Ill., graph. Darst. |
Internformat
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Datensatz im Suchindex
| _version_ | 1804149183459885056 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
ABSTRACT III
KURZFASSUNG V
CONTENTS VII
LIST OF FIGURES XI
LIST OF TABLES XV
LIST OF ALGORITHMS XVII
1 INTRODUCTION 1
1.1 O N A I PLANNING 4
1.2 GENEALOGY 6
1.3 CLASSICAL PLANNING 7
1.4 STATE O F THE ART 10
1.5 MOTIVATION 10
1.6 CONTRIBUTIONS 13
2 PRELIMINARIES 17
2.1 CLASSICAL PLANNING 17
2.1.1 PROPOSITIONAL STRIPS 17
2.1.2 THE APARTMENT DOMAIN 19
2.1.3 CLASSICAL PLANNING - ASSUMPTIONS, CLASSIFICATION AND COMPLEXITY .
. 21
2.2 CLASSICAL PLANNING AS A PROBLEM O F COMBINATORIAL OPTIMIZATION 26
2.3 GRAPH-TRAVERSAL ALGORITHMS 27
2.3.1 DEPTH-FIRST SEARCH 27
2.3.2 BREADTH-FIRST SEARCH 30
2.4 TERMINOLOGY AND CONVENTIONS 32
VII
HTTP://D-NB.INFO/1026739772
IMAGE 2
2.5 HEURISTICS 33
2.5.1 ADMISSIBILITY 35
2.5.2 BEST-FIRST SEARCH AND A * 35
2.5.3 CONSISTENCY 38
3 TARGET VALUE SEARCH 41
3.1 EXAMPLE DOMAINS 41
3.1.1 PERVASIVE DIAGNOSIS FOR MANUFACTURING SYSTEMS 41
3.1.2 CONSUMER RECOMMENDER SYSTEMS 4 3
3.2 PROBLEM DEFINITION 4 4
3.2.1 CONVENTIONS 45
3.2.2 COMPLEXITY 4 6
3.3 HEURISTICS FOR TARGET VALUE SEARCH 47
3.3.1 A STRAIGHTFORWARD APPROACH 47
3.3.2 A N ADMISSIBLE ESTIMATOR FOR TARGET VALUE SEARCH 4 8
3.3.3 MULTI-INTERVAL HEURISTIC FOR TARGET VALUE SEARCH 50
3.3.4 COMPUTING THE INTERVAL STORE 51
3.4 ALGORITHMS FOR TARGET VALUE SEARCH 53
3.4.1 BEST-FIRST TARGET VALUE SEARCH 54
3.4.2 DEPTH-FIRST TARGET VALUE SEARCH 56
3.5 EMPIRICAL EVALUATION 59
3.5.1 THE TEST DOMAINS 60
3.5.2 COMPARISON O F HS, BFTVS AND DFTVS 61
3.5.3 SCALING O F DFTVS 64
3.5.4 INTERVAL STORE EVALUATION 66
3.6 SUMMARY 68
4 STATE-SET REPRESENTATION 71
4.1 BACKGROUND 71
4.1.1 PATTERN DATABASES 71
4.1.2 STATE REPRESENTATION 72
4.1.3 STATE-SETS IN UNIT-COST BEST-FIRST SEARCH 7 5
4.1.4 SET-REPRESENTATION TECHNIQUES 76
4.1.5 EXPLICIT SET REPRESENTATIONS 77
4.1.6 IMPLICIT SET REPRESENTATIONS 78
IMAGE 3
4.2 LOES - THE LEVEL-ORDERED EDGE SEQUENCE 82
4.2.1 CONVENTIONS 84
4.2.2 PREFIX TREE MINIMIZATION 85
4.2.3 SAMPLING REPRESENTATIVE STATES 85
4.2.4 ANALYZING THE SAMPLE SET 87
4.2.5 ON PREFIX TREE ENCODINGS 87
4.2.6 THE LOES ENCODING 9 3
4.2.7 SIZE BOUNDS O F LOES ENCODINGS 9 4
4.2.8 MAPPING TREE NAVIGATION AND SET OPERATIONS TO LOES 95
4.2.9 BUILDING A DYNAMIC DATA-STRUCTURE FOR DYNAMIC PROGRAMMING BASED
ON LOES 102
4.2.10 CONSTRUCTION O F A LOES CODE FROM A LEXICOGRAPHICALLY-ORDERED
KEYSEQUENCE 107
4.2.11 VIRTUAL IN-PLACE MERGING 109
4.2.12 PRACTICAL OPTIMIZATIONS 110
4.2.13 EMPIRICAL COMPARISON O F LOES AND B D D IN BFS-DD 112
4.2.14 PATTERN DATABASE REPRESENTATIONS 120
4.2.15 COMBINED LAYER SETS 120
4.2.16 INVERSE RELATION 121
4.2.17 COMPRESSED LOES 121
4.2.18 EMPIRICAL COMPARISON O F LOES AND B D D FOR PATTERN DATABASE REP
RESENTATIONS 123
4.3 SUMMARY 137
5 PARALLELIZATION OF HEURISTIC SEARCH 139
5.1 BACKGROUND 139
5.1.1 PARALLEL STRUCTURED DUPLICATE DETECTION 142
5.2 PARALLEL EDGE PARTITIONING 143
5.2.1 PARALLEL BREADTH-FIRST SEARCH WITH DUPLICATE DETECTION AND EDGE
PAR
TITIONING - AN INTEGRATION EXAMPLE 147
5.3 EMPIRICAL COMPARISON O F PEP, PSDD, P B N F AND HDA* 152
5.3.1 SUCCESSOR GENERATION 152
5.3.2 PERFORMANCE AND SCALING 153
5.4 SUMMARY 160
6 CONCLUSION 161
IMAGE 4
7 OUTLOOK
BIBLIOGRAPHY
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| spelling | Schmidt, Tim-Christian Verfasser (DE-588)1026739640 aut Representation and parallelization techniques for classical planning Tim-Christian Schmidt 2012 XVII, 181 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier München, Techn. Univ., Diss., 2012 Agent Künstliche Intelligenz (DE-588)4310355-8 gnd rswk-swf Suchverfahren (DE-588)4132315-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Agent Künstliche Intelligenz (DE-588)4310355-8 s Suchverfahren (DE-588)4132315-4 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:91-diss-20120404-1083789-1-4 http://mediatum.ub.tum.de/node?id=1083789 Verlag kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20120404-1083789-1-4 Resolving-System DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025012690&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Schmidt, Tim-Christian Representation and parallelization techniques for classical planning Agent Künstliche Intelligenz (DE-588)4310355-8 gnd Suchverfahren (DE-588)4132315-4 gnd |
| subject_GND | (DE-588)4310355-8 (DE-588)4132315-4 (DE-588)4113937-9 |
| title | Representation and parallelization techniques for classical planning |
| title_auth | Representation and parallelization techniques for classical planning |
| title_exact_search | Representation and parallelization techniques for classical planning |
| title_full | Representation and parallelization techniques for classical planning Tim-Christian Schmidt |
| title_fullStr | Representation and parallelization techniques for classical planning Tim-Christian Schmidt |
| title_full_unstemmed | Representation and parallelization techniques for classical planning Tim-Christian Schmidt |
| title_short | Representation and parallelization techniques for classical planning |
| title_sort | representation and parallelization techniques for classical planning |
| topic | Agent Künstliche Intelligenz (DE-588)4310355-8 gnd Suchverfahren (DE-588)4132315-4 gnd |
| topic_facet | Agent Künstliche Intelligenz Suchverfahren Hochschulschrift |
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