Probabilistic cognition for technical systems: statistical relational models for high-level knowledge representation, learning and reasoning
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
2012
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| Schlagworte: | |
| Online-Zugang: | kostenfrei https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20120823-1096684-0-5 Inhaltsverzeichnis |
| Beschreibung: | XVI, 262 S. Ill., graph. Darst. |
Internformat
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| 245 | 1 | 0 | |a Probabilistic cognition for technical systems |b statistical relational models for high-level knowledge representation, learning and reasoning |c Dominik Jain |
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Datensatz im Suchindex
| _version_ | 1804150098277433344 |
|---|---|
| adam_text | IMAGE 1
CONTENTS
ABSTRACT III
KURZFASSUNG V
ACKNOWLEDGEMENTS VII
CONTENTS IX
LIST OF RESOURCES XIII
1 INTRODUCTION 1
1.1 CONTRIBUTIONS & READER S GUIDE 6
1.2 NOTATION 11
2 FROM PROPOSITIONAL MODELS TO STATISTICAL RELATIONAL MODELS 13 2.1
LOGIC 13
2.1.1 PROPOSITIONAL LOGIC 14
2.1.2 FIRST-ORDER LOGIC 15
2.2 GRAPHICAL MODELS 17
2.2.1 MARKOV NETWORKS 18
2.2.2 BAYESIAN NETWORKS 24
2.2.3 CONSTRAINT NETWORKS 27
2.2.4 MIXED NETWORKS 27
2.2.5 SEQUENCE MODELS 30
2.3 STATISTICAL RELATIONAL MODELS 32
2.3.1 EARLY PROBABILISTIC LOGICS 32
2.3.2 THE PRINCIPLES OF TEMPLATE MODELS 34
2.3.3 AN OVERVIEW OF FIRST-ORDER PROBABILISTIC LANGUAGES 35
IX
HTTP://D-NB.INFO/1033152838
IMAGE 2
CONTENTS
3 MARKOV LOGIC NETWORKS 41
3.1 FORMALISM 42
3.1.1 FEATURE GRANULARITY AND QUANTIFIER SEMANTICS 44
3.1.2 PROBABILISTIC SEMANTICS 45
3.2 INFERENCE 46
3.2.1 POSTERIOR MARGINALS 47
3.2.2 MOST PROBABLE EXPLANATION 53
3.3 LEARNING 53
3.4 KNOWLEDGE ENGINEERING 59
3.4.1 SEMANTIC PERSPECTIVES 60
3.4.2 FORMULA WEIGHTS 61
3.4.3 THE PROBABILISTIC IMPLICATION FALLACY 65
3.4.4 SHALLOW TRANSFER 70
3.4.5 DISCUSSION 74
3.5 MARKOV LOGIC NETWORKS WITH PROBABILITY CONSTRAINTS 75
3.5.1 ITERATIVE PROPORTIONAL FITTING 76
3.5.2 FITTING AT THE MODEL LEVEL 78
3.6 ADAPTIVE MARKOV LOGIC NETWORKS 82
3.6.1 THE IMPACT OF CARDINALITY RESTRICTIONS 83
3.6.2 DEFINITION 86
3.6.3 PARAMETER LEARNING 86
3.6.4 EXPLICIT CARDINALITY CONSTRAINTS 90
3.6.5 EXPERIMENTS 92
3.6.6 DISCUSSION 95
4 UNCERTAIN EVIDENCE AND PROBABILISTIC INFORMATION INTERCHANGE 97 4.1 ON
THE SEMANTICS OF UNCERTAIN EVIDENCE 98
4.1.1 VIRTUAL EVIDENCE 99
4.1.2 SOFT EVIDENCE 100
4.1.3 DISCUSSION 101
4.2 SOFT EVIDENTIAL UPDATE 102
4.2.1 PROBABILITY CONSTRAINTS AND ITERATIVE FITTING 102
4.2.2 A MARKOV CHAIN MONTE CARLO METHOD 104
4.2.3 EXPERIMENTS 107
X
IMAGE 3
CONTENTS
4.3 LEARNING WITH SOFT EVIDENCE 112
4.3.1 LEARNING WITH SOFT FEATURES 113
4.3.2 SAMPLING-BASED LEARNING 116
5 BAYESIAN LOGIC NETWORKS 121
5.1 FORMALISM 122
5.2 REPRESENTATION IN PRACTICE 127
5.2.1 FUNDAMENTAL DECLARATIONS 127
5.2.2 CONDITIONAL PROBABILITY FRAGMENTS 129
5.2.3 LOGICAL FORMULAS 136
5.2.4 EVIDENCE 137
5.2.5 DISCUSSION 139
5.3 APPROACHES TO LEARNING AND INFERENCE 140
5.3.1 LEARNING 140
5.3.2 INFERENCE 143
5.4 SAMPLING-BASED INFERENCE 144
5.4.1 FUNDAMENTAL SAMPLING TECHNIQUES 144
5.4.2 BACKWARD SAMPLESEARCH 148
5.4.3 SAMPLESEARCH WITH ABSTRACT CONSTRAINT LEARNING 151
5.4.4 EXPERIMENTS 153
5.4.5 ESTIMATING APPROXIMATION QUALITY 155
6 APPLICATIONS 163
6.1 ROBOT PERCEPTION 163
6.1.1 CATEGORISATION OF KITCHEN OBJECTS 163
6.1.2 DYNAMIC WORLD STATE LOGGING 173
6.2 COGNITION-ENABLED CONTROL OF AUTONOMOUS ROBOTS 184
6.2.1 MODELS OF HUMAN EVERYDAY ACTIVITIES AND ENVIRONMENTS . . . . 188
6.2.2 PLAN PARAMETRISATION 205
6.2.3 A ROBOT SYSTEM THAT COMBINES PERCEPTION, KNOWLEDGE PRO CESSING
AND PROBABILISTIC REASONING 210
6.2.4 LEARNING FROM LOGGED EXECUTION TRACES 215
6.3 PLAN ASSESSMENT IN MANUFACTURING 224
6.3.1 A STATISTICAL RELATIONAL MODEL OF PRODUCTION PLANT BEHAVIOUR . .
225
6.3.2 CONFIDENCE BOUNDS FOR APPROXIMATE PLAN ASSESSMENT 228
XI
IMAGE 4
CONTENTS
6.3.3 TRANSLATING AI ENGINEERING MODELS INTO STATISTICAL RELATIONAL
MODELS 231
7 CONCLUSION 239
BIBLIOGRAPHY 247
INDEX 260
XII
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| spelling | Jain, Dominik Verfasser aut Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning Dominik Jain 2012 XVI, 262 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier München, Techn. Univ., Diss., 2012 Wissensrepräsentationssprache (DE-588)4138762-4 gnd rswk-swf Kognition (DE-588)4031630-0 gnd rswk-swf Robotik (DE-588)4261462-4 gnd rswk-swf Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Inferenz Künstliche Intelligenz (DE-588)4333533-0 s Wissensrepräsentationssprache (DE-588)4138762-4 s Kognition (DE-588)4031630-0 s Robotik (DE-588)4261462-4 s DE-604 Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:91-diss-20120823-1096684-0-5 http://mediatum.ub.tum.de/node?id=1096684 Verlag kostenfrei Volltext https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20120823-1096684-0-5 Resolving-System DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025747168&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Jain, Dominik Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning Wissensrepräsentationssprache (DE-588)4138762-4 gnd Kognition (DE-588)4031630-0 gnd Robotik (DE-588)4261462-4 gnd Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd |
| subject_GND | (DE-588)4138762-4 (DE-588)4031630-0 (DE-588)4261462-4 (DE-588)4333533-0 (DE-588)4113937-9 |
| title | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning |
| title_auth | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning |
| title_exact_search | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning |
| title_full | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning Dominik Jain |
| title_fullStr | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning Dominik Jain |
| title_full_unstemmed | Probabilistic cognition for technical systems statistical relational models for high-level knowledge representation, learning and reasoning Dominik Jain |
| title_short | Probabilistic cognition for technical systems |
| title_sort | probabilistic cognition for technical systems statistical relational models for high level knowledge representation learning and reasoning |
| title_sub | statistical relational models for high-level knowledge representation, learning and reasoning |
| topic | Wissensrepräsentationssprache (DE-588)4138762-4 gnd Kognition (DE-588)4031630-0 gnd Robotik (DE-588)4261462-4 gnd Inferenz Künstliche Intelligenz (DE-588)4333533-0 gnd |
| topic_facet | Wissensrepräsentationssprache Kognition Robotik Inferenz Künstliche Intelligenz Hochschulschrift |
| url | http://mediatum.ub.tum.de/node?id=1096684 https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20120823-1096684-0-5 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025747168&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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