Neural symbolic learning systems: foundations and applications
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong
Springer
2002
|
| Schriftenreihe: | Perspectives in neural computing
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Erscheint: Mai 2002 |
| Beschreibung: | XIII, 271 S. Ill., graph. Darst. |
| ISBN: | 1852335122 |
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| 245 | 1 | 0 | |a Neural symbolic learning systems |b foundations and applications |c Artur S. D'Avila Garcez, Krysia B. Broda and Dov M. Gabby |
| 264 | 1 | |a London ; Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong |b Springer |c 2002 | |
| 300 | |a XIII, 271 S. |b Ill., graph. Darst. | ||
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Datensatz im Suchindex
| _version_ | 1804129130553278464 |
|---|---|
| adam_text | TABLE OF CONTENTS 1. INTRODUCTION AND OVERVIEW
............................... 1 1.1 WHY INTEGRATE NEURONS AND SYMBOLS?
. . . . . . . . . . . . . . . . . . . . . 1 1.2 STRATEGIES OF
NEURAL-SYMBOLIC INTEGRATION . . . . . . . . . . . . . . . . . . 3 1.3
NEURAL-SYMBOLIC LEARNING SYSTEMS . . . . . . . . . . . . . . . . . . . .
. . . 5 1.4 A SIMPLE EXAMPLE . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 7 1.5 HOW TO READ THIS BOOK . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.6 SUMMARY . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . 12 2. BACKGROUND
.............................................. 13 2.1 GENERAL
PRELIMINARIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 13 2.2 INDUCTIVE LEARNING . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . 14 2.3 NEURAL NETWORKS . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.3.1 ARCHITECTURES . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . 16 2.3.2 LEARNING STRATEGY . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 19 2.3.3 RECURRENT NETWORKS . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 LOGIC
PROGRAMMING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 23 2.4.1 WHAT IS LOGIC PROGRAMMING? . . . . . . . . . . .
. . . . . . . . . . . 23 2.4.2 FIXPOINTS AND DEFINITE PROGRAMS . . . . .
. . . . . . . . . . . . . . 26 2.5 NONMONOTONIC REASONING . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.1 STABLE MODELS
AND ACCEPTABLE PROGRAMS . . . . . . . . . . . . 29 2.6 BELIEF REVISION .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 34 2.6.1 TRUTH MAINTENANCE SYSTEMS . . . . . . . . . . . . . . .
. . . . . . . . 37 2.6.2 COMPROMISE REVISION . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 39 PART I. KNOWLEDGE REFINEMENT IN NEURAL
NETWORKS 3. THEORY REFINEMENT IN NEURAL NETWORKS .................. 43
3.1 INSERTING BACKGROUND KNOWLEDGE . . . . . . . . . . . . . . . . . . .
. . . . . . 44 3.2 MASSIVELY PARALLEL DEDUCTION. . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 56 3.3 PERFORMING INDUCTIVE LEARNING . .
. . . . . . . . . . . . . . . . . . . . . . . . . 58 3.4 ADDING
CLASSICAL NEGATION . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 59 XII TABLE OF CONTENTS 3.5 ADDING METALEVEL PRIORITIES . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.6 SUMMARY AND
FURTHER READING . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4. EXPERIMENTS ON THEORY REFINEMENT ...................... 87 4.1 DNA
SEQUENCE ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 87 4.2 POWER SYSTEMS FAULT DIAGNOSIS . . . . . . . . . . . .
. . . . . . . . . . . . . . . 97 4.3 DISCUSSION . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.4 APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . 108 PART II. KNOWLEDGE EXTRACTION FROM
NEURAL NETWORKS 5. KNOWLEDGE EXTRACTION FROM TRAINED NETWORKS
............ 113 5.1 THE EXTRACTION PROBLEM. . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 114 5.2 THE CASE OF REGULAR NETWORKS
. . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.2.1 POSITIVE
NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
121 5.2.2 REGULAR NETWORKS . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 127 5.3 THE GENERAL CASE EXTRACTION . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 137 5.3.1 REGULAR SUBNETWORKS . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5.3.2
KNOWLEDGE EXTRACTION FROM SUBNETWORKS . . . . . . . . . . . . 139 5.3.3
ASSEMBLING THE FINAL RULE SET . . . . . . . . . . . . . . . . . . . . .
151 5.4 KNOWLEDGE REPRESENTATION ISSUES . . . . . . . . . . . . . . . .
. . . . . . . . 153 5.5 SUMMARY AND FURTHER READING . . . . . . . . . .
. . . . . . . . . . . . . . . . . 155 6. EXPERIMENTS ON KNOWLEDGE
EXTRACTION ................... 159 6.1 IMPLEMENTATION . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 6.2
THE MONK*S PROBLEMS . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 166 6.3 DNA SEQUENCE ANALYSIS . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . 168 6.4 POWER SYSTEMS FAULT
DIAGNOSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 6.5
DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . 176 PART III. KNOWLEDGE REVISION IN NEURAL
NETWORKS 7. HANDLING INCONSISTENCIES IN NEURAL NETWORKS .............
183 7.1 THEORY REVISION IN NEURAL NETWORKS . . . . . . . . . . . . . . .
. . . . . . . 183 7.1.1 THE EQUIVALENCE WITH TRUTH MAINTENANCE SYSTEMS .
. . . 184 7.1.2 MINIMAL LEARNING . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 186 7.2 SOLVING INCONSISTENCIES IN NEURAL
NETWORKS . . . . . . . . . . . . . . . . 192 7.2.1 COMPROMISE REVISION .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 7.2.2
FOUNDATIONAL REVISION . . . . . . . . . . . . . . . . . . . . . . . . .
. . . 195 7.2.3 NONMONOTONIC THEORY REVISION . . . . . . . . . . . . . .
. . . . . . 200 7.3 SUMMARY OF THE CHAPTER . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . 207 TABLE OF CONTENTS XIII 8.
EXPERIMENTS ON HANDLING INCONSISTENCIES ................. 209 8.1
REQUIREMENTS SPECIFICATIONS EVOLUTION AS THEORY REFINEMENT 209 8.1.1
ANALYSING SPECIFICATIONS . . . . . . . . . . . . . . . . . . . . . . . .
. . . 209 8.1.2 REVISING SPECIFICATIONS . . . . . . . . . . . . . . . .
. . . . . . . . . . . . 212 8.2 THE AUTOMOBILE CRUISE CONTROL SYSTEM . .
. . . . . . . . . . . . . . . . . 215 8.2.1 KNOWLEDGE INSERTION . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . 217 8.2.2 KNOWLEDGE
REVISION: HANDLING INCONSISTENCIES . . . . . . . . 219 8.2.3 KNOWLEDGE
EXTRACTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
8.3 DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 228 8.4 APPENDIX . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 9.
NEURAL-SYMBOLIC INTEGRATION: THE ROAD AHEAD ........... 235 9.1
KNOWLEDGE EXTRACTION. . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . 237 9.2 ADDING DISJUNCTIVE INFORMATION . . . . . . . .
. . . . . . . . . . . . . . . . . . 240 9.3 EXTENSION TO THE FIRST-ORDER
CASE . . . . . . . . . . . . . . . . . . . . . . . . 244 9.4 ADDING
MODALITIES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 245 9.5 NEW PREFERENCE RELATIONS. . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 247 9.6 A PROOF THEORETICAL
APPROACH . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 9.7
THE *FORBIDDEN ZONE* [ A MAX ,A MIN ] . . . . . . . . . . . . . . . . .
. . . . . . 250 9.8 ACCEPTABLE PROGRAMS AND NEURAL NETWORKS . . . . . .
. . . . . . . . . . 250 9.9 EPILOGUE . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 REFERENCES
.................................................... 253 INDEX
......................................................... 267
|
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| author | D'Avila Garcez, Artur S. 1970- Broda, Krysia Gabbay, Dov M. 1945- |
| author_GND | (DE-588)131692348 (DE-588)124196314 |
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| dewey-full | 006.3/2 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
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| discipline | Informatik |
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| illustrated | Illustrated |
| indexdate | 2024-07-09T19:00:15Z |
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| isbn | 1852335122 |
| language | English |
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| publisher | Springer |
| record_format | marc |
| series2 | Perspectives in neural computing |
| spelling | D'Avila Garcez, Artur S. 1970- Verfasser (DE-588)131692348 aut Neural symbolic learning systems foundations and applications Artur S. D'Avila Garcez, Krysia B. Broda and Dov M. Gabby London ; Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong Springer 2002 XIII, 271 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Perspectives in neural computing Erscheint: Mai 2002 Kunstmatige intelligentie gtt Neurale netwerken gtt Künstliche Intelligenz Artificial intelligence Neural networks (Computer science) Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Symbolverarbeitung (DE-588)4278565-0 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 gnd rswk-swf Hybrides System (DE-588)4510314-8 gnd rswk-swf Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Hybrides System (DE-588)4510314-8 s Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s Symbolverarbeitung (DE-588)4278565-0 s Wissensextraktion (DE-588)4546354-2 s Logische Programmierung (DE-588)4195096-3 s DE-604 Broda, Krysia Verfasser aut Gabbay, Dov M. 1945- Verfasser (DE-588)124196314 aut SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009765701&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | D'Avila Garcez, Artur S. 1970- Broda, Krysia Gabbay, Dov M. 1945- Neural symbolic learning systems foundations and applications Kunstmatige intelligentie gtt Neurale netwerken gtt Künstliche Intelligenz Artificial intelligence Neural networks (Computer science) Maschinelles Lernen (DE-588)4193754-5 gnd Symbolverarbeitung (DE-588)4278565-0 gnd Logische Programmierung (DE-588)4195096-3 gnd Hybrides System (DE-588)4510314-8 gnd Wissensextraktion (DE-588)4546354-2 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4278565-0 (DE-588)4195096-3 (DE-588)4510314-8 (DE-588)4546354-2 (DE-588)4226127-2 |
| title | Neural symbolic learning systems foundations and applications |
| title_auth | Neural symbolic learning systems foundations and applications |
| title_exact_search | Neural symbolic learning systems foundations and applications |
| title_full | Neural symbolic learning systems foundations and applications Artur S. D'Avila Garcez, Krysia B. Broda and Dov M. Gabby |
| title_fullStr | Neural symbolic learning systems foundations and applications Artur S. D'Avila Garcez, Krysia B. Broda and Dov M. Gabby |
| title_full_unstemmed | Neural symbolic learning systems foundations and applications Artur S. D'Avila Garcez, Krysia B. Broda and Dov M. Gabby |
| title_short | Neural symbolic learning systems |
| title_sort | neural symbolic learning systems foundations and applications |
| title_sub | foundations and applications |
| topic | Kunstmatige intelligentie gtt Neurale netwerken gtt Künstliche Intelligenz Artificial intelligence Neural networks (Computer science) Maschinelles Lernen (DE-588)4193754-5 gnd Symbolverarbeitung (DE-588)4278565-0 gnd Logische Programmierung (DE-588)4195096-3 gnd Hybrides System (DE-588)4510314-8 gnd Wissensextraktion (DE-588)4546354-2 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Kunstmatige intelligentie Neurale netwerken Künstliche Intelligenz Artificial intelligence Neural networks (Computer science) Maschinelles Lernen Symbolverarbeitung Logische Programmierung Hybrides System Wissensextraktion Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009765701&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT davilagarcezarturs neuralsymboliclearningsystemsfoundationsandapplications AT brodakrysia neuralsymboliclearningsystemsfoundationsandapplications AT gabbaydovm neuralsymboliclearningsystemsfoundationsandapplications |