Learning as problem solving:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
2001
|
| Schriftenreihe: | SIKS dissertation series
2001,3 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | IV, 222 S. Ill. |
| ISBN: | 9090145125 |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Titel: Learning as problem solving
Autor: Someren, Maarten W. van
Jahr: 2001
Contents
1 Introduction 1
2 Viewpoints on learning 7
2.1 Formalising and automating knowledge acquisition.......... 7
2.2 Machine learning............................. 8
2.2.1 Learning as storage and retrieval................ 9
2.2.2 Learning as chunking and storage................ 9
2.2.3 Domain and task specific learning goals and operators .... 10
2.2.4 Learning as improving correctness, effectiveness and abstrac-
tion level............................. 11
2.2.5 Classifications of machine learning systems.......... 11
2.3 Systems that integrate learning systems................ 19
2.4 Chapter conclusion............................ 22
3 A language for modelling learning systems 23
3.1 Modelling learning systems....................... 23
3.2 Modelling knowledge systems in LML................. 27
3.3 Properties of knowledge systems .................... 27
3.4 Problem solver.............................. 30
3.5 Properties of the form of a knowledge base .............. 31
3.5.1 Knowledge Base Grammar (kbGrammar)........... 32
3.5.2 Knowledge base vocabulary (kbVocabulary).......... 32
3.5.3 Knowledge Base Lexicon (kbLexicon) ............. 33
3.6 Properties of the function........................ 34
3.6.1 Problem grammar (pGrammar)................. 35
3.6.2 Problem vocabulary (pVocabulary) and task vocabulary (taskVo-
cabulary)............................. 36
3.6.3 Problem lexicon (pLexicon) and Problem Lexical Structure
(pLexicalStracture)........................ 36
3.6.4 Solutions............................. 37
3.7 Technical properties........................... 38
3.7.1 Size................................ 38
3.7.2 Solution time........................... 38
3.8 An LML model of a compound knowledge system........... 39
3.9 Sources .................................. 40
3.10 Learning goals .............................. 43
3.11 Modelling learning systems in LML................... 46
3.12 Discussion................................. 49
3.12.1 The scope of LML........................ 49
3.12.2 Related models.......................... 54
3.13 Chapter conclusion............................ 55
Modelling the function of learning systems 57
4.1 The modelled systems.......................... 57
4.1.1 Method for constructing the models.............. 59
4.2 AQTT-15................................. 60
4.2.1 The AQ15 system ........................ 61
4.2.2 The TRUNC component..................... 63
4.3 Learning decision trees.......................... 66
4.4 Explanation-based learning....................... 69
4.4.1 PROLOG-EBL and ROE.................... 71
4.4.2 LEAP............................... 71
4.4.3 Learning ACES.......................... 72
4.4.4 BAGGER: EBL for iterative concepts............. 73
4.4.5 Prieditis system......................... 74
4.4.6 Discussion of EBL systems ................... 74
4.5 Refinement systems............................ 75
4.5.1 Duval s refinement system.................... 75
4.5.2 ML-SMART ........................... 78
4.5.3 OGUST.............................. 78
4.6 PROTOS................................. 79
4.7 The INDE refinement system...................... 80
4.8 ABD.................................... 81
4.9 Discussion of the LML models of learning systems.......... 82
4.9.1 Learning as constructing knowledge systems.......... 82
4.9.2 The LML properties....................... 84
4.9.3 LML datatypes.......................... 88
4.9.4 Elicitation............................. 89
4.9.5 Other operations on knowledge................. 90
4.10 Recent ML systems............................ 90
4.11 Chapter conclusions........................... 92
5 The KBL method and system 95
5.1 The Knowledge Base Learner...................... 95
5.2 Overview of the example scenario.................... 97
5.3 Toplevel control of learning....................... 103
5.4 Constructing the LML model of a knowledge system......... Ill
5.4.1 Problem solver.......................... Ill
5.4.2 Knowledge base grammar.................... 112
5.4.3 Knowledge base vocabulary................... 115
5.4.4 Solutions............................. 115
5.4.5 Problem grammar........................ 115
5.4.6 Problem vocabulary and task vocabulary........... 116
5.4.7 Problem lexical structure.................... 117
5.4.8 Function.............................. 117
5.4.9 Size................................ 118
5.4.10 Solution time........................... 118
5.5 Examples of LML models........................ 118
5.6 LML models of sources.......................... 120
5.7 Relations between knowledge systems ................. 122
5.8 Learning goals.............................. 124
5.9 Testing if a learning goal is satisfied.................. 127
5.10 Find task definition, select relevant knowledge systems and find miss-
ing knowledge............................... 129
5.11 Elicitation................................. 130
5.12 Ordering knowledge systems....................... 132
5.13 Find best knowledge system and operator that improve input .... 134
5.14 Learning operators in KBL....................... 136
5.14.1 Conversion operators....................... 137
5.14.2 Knowledge integration operators................ 140
5.14.3 Generalisation operators..................... 142
5.14.4 Speedup learning operators................... 143
5.14.5 Learning operators for compound knowledge systems..... 143
5.14.6 abductiveDecompose....................... 144
5.14.7 Merge operators ......................... 145
5.14.8 Configuration operators..................... 145
5.15 The example continued.......................... 145
5.16 An example of learning compound systems .............. 150
6 Evaluating KBL 155
6.1 Introduction................................ 155
6.2 The efficiency of KBL.......................... 155
6.3 The functionality of KBL........................ 160
iii
6.3.1 Multistrategy learning systems................. 160
6.3.2 The ACKnowledge KEW system................ 165
6.3.3 The ACKnowledge GRANUL system.............. 173
6.3.4 The MOBAL system....................... 176
6.3.5 The MLT toolkit......................... 177
6.3.6 The MUSKRAT system..................... 178
6.4 Discussion................................. 179
7 Discussion and conclusions 181
7.1 Our view of learning........................... 181
7.2 The LML language............................ 182
7.3 The KBL architecture.......................... 185
7.4 KBL as an open architecture...................... 187
7.5 Prospects for practical applications................... 188
7.6 Open issues................................ 189
7.7 Concluding remarks........................... 190
8 Summary 191
9 Samenvatting 195
A LML models of learning operators 199
B Details of data for example 207
References 209
IV
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| spelling | Someren, Maarten W. van 1955- Verfasser (DE-588)115263020 aut Learning as problem solving door Maarten Willem van Someren Amsterdam 2001 IV, 222 S. Ill. txt rdacontent n rdamedia nc rdacarrier SIKS dissertation series 2001,3 Amsterdam, Univ., Diss., 2001 Hochschulschrift gtt Lernen (DE-588)4035408-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Problemlösen (DE-588)4076358-4 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Lernen (DE-588)4035408-8 s Problemlösen (DE-588)4076358-4 s DE-604 Künstliche Intelligenz (DE-588)4033447-8 s DE-188 SIKS dissertation series 2001,3 (DE-604)BV012842495 2001,3 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009912150&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Someren, Maarten W. van 1955- Learning as problem solving SIKS dissertation series Hochschulschrift gtt Lernen (DE-588)4035408-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Problemlösen (DE-588)4076358-4 gnd |
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| title | Learning as problem solving |
| title_auth | Learning as problem solving |
| title_exact_search | Learning as problem solving |
| title_full | Learning as problem solving door Maarten Willem van Someren |
| title_fullStr | Learning as problem solving door Maarten Willem van Someren |
| title_full_unstemmed | Learning as problem solving door Maarten Willem van Someren |
| title_short | Learning as problem solving |
| title_sort | learning as problem solving |
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| topic_facet | Hochschulschrift Lernen Künstliche Intelligenz Problemlösen |
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