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Nienhuys-Cheng S., Wolf R. — Foundations of Inductive Logic Programming
Nienhuys-Cheng S., Wolf R. — Foundations of Inductive Logic Programming



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Название: Foundations of Inductive Logic Programming

Авторы: Nienhuys-Cheng S., Wolf R.

Аннотация:

Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: 1

Год издания: 1997

Количество страниц: 410

Добавлена в каталог: 13.12.2013

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
Grammar (for language bias)      347
Greatest lower bound (glb)      221 225
Greatest specialization (GS)      xii 225
Greatest specialization (GS) in first-order logic      275
Greatest specialization (GS) of atoms      227
Greatest specialization (GS) under implication (GSI)      275 276
Greatest specialization (GS) under implication (GSI) for Horn clauses      276
Greatest specialization (GS) under subsumption (GSS)      251
Greatest specialization (GS) under subsumption (GSS) for Horn clauses      251
Grobelnik, M.      316 355
Ground atom      46
Ground formula      22
Ground instance      59
Ground substitution      59
Ground term      22 46
GS      see "Greatest specialization"
GSI      see "Greatest specialization under implication"
GSS      see "Greatest specialization under subsumption"
Gunetti, D.      x 197 347 357 358
Gyimothy, T.      357
h-easiness      193
Hanschke, P.      125
Haussler, D.      326
Head (of a clause)      106 135
Helft, N.      172
Hempel, C.      174 175n
Herbrand base      46
Herbrand interpretation      xi 35 47 172 273 290 340
Herbrand model      48 112
Herbrand pre-interpretation      46
Herbrand universe      46
Herbrand's theorem      79
Herbrand, J.      45
Hierarchical program      153
Higher-order logic      18n
Hill, R.      106
Hopcroft, J.      55 338n
Horn clause      xi 55n 102 105 106
Horn language ($\mathcal{H}$)      106
Horn, A.      105
Horvath, T.      x 338 339
Hume, D. (philosopher)      174
Hume, D. (researcher)      197
Hypothesis language ($\mathcal{C}_{h}$)      180
ID3      354
Identification from equivalence queries      331
Identification in the limit      194 321
Identity substitution      59
Idestam-Almquist, P.      197 208 210 266 357
Iff (if, and only if)      11
ij-determinate clause      336 347 355
ij-nondeterminate clause      336
ILP      see "Inductive logic programming"
Imperfect data      352
Implementation of ILP      xiii 346 354
Implication      xi xii 10 11 30 49 50 55 191 265 273 280 287 304 317 352 363
Implication for atoms      226
Implication is a quasi-order      265n
Implication, connective      6 11
Implication, non-clausal      87
Imput      357
Incomparable      221
Incompatible clauses      243 312
Incompleteness of input resolution      100 101
Incompleteness of SLDNF-resolution      153
Inconsistent      12 31
Incremental learning      171 354 355
Induction      ix 163 197
Inductive inference      175
Inductive logic programming      ix 36 55 154 164 167 173 175 222 225 345
Inductive logic programming, history of      174
Infinite branch      123
Initial pre-SLDNF-tree      138
Inoue, K.      90 94
Input clause      100 107
Input deduction      100 128
Input derivation      100
Input refutation      100
Input resolution      100
Instance      59 61
Instance of a concept      179
Instance set      268
Integrity constraint      356
Intension of a relation      179
Interactive learning      171 354 355
Interpretation      xi 7 23 26
Invalid      12 31
Inverse reduction      249
Inverse Reduction Algorithm      250 311 315
Inverse resolution      xii 176 197 207 220n 319 349 354
Inverse resolution for program restructuring      348
Inverse substitution      238
Ishizaka, H.      225n 337
Itou      354
Jaffar, J.      153 359
Jeffrey, R.      18 33 56 168
Jevons, S.      174
Jezernik, A.      358
Jigsaw      357
jk-clausal theory      341
Johnson, D.      246n 326 338n
k-ary recursive clause      338
k-clause program      335
k-literal clause      335
k-literal program      335
Kakas, A.      173
Kearns, M.      330 332
Kietz, J-U.      x 246n 334 338 356
King, R.      357 358
Knowledge discovery      172
Kolmogorov complexity      353
Komorowski, H.      210
Kononenko, L.      355
Kowalski, R.      55 76 90 94 105n 173
Krizman, V.      358
Kuehner, D.      90 94 105n
Label of an example      323
Laird, P.      220 305 331
Language bias      xiii 171 346
Language bias, shift      347 349
Lapointe, S.      357
Lassez, J-L.      65 153
Lattice      222
Lattice for atoms      231
Lattice under implication      276
Lattice under subsumption      255 256
Lavrac, N.      x 176 300 352 353 355 358
Learnability theory      321
Least generalization (LG)      xii 175 205 225
Least generalization (LG) in first-order logic      275
Least generalization (LG) of atoms      230
Least generalization (LG) under atomic generalization (LGA)      244 252 253
Least generalization (LG) under generalized subsumption (LGGS)      280n 294 295
Least generalization (LG) under implication (LGI)      265 266 272 273 279
Least generalization (LG) under implication (LGI) for Horn clauses      267
Least generalization (LG) under implication (LGI), computability of      275
Least generalization (LG) under implication (LGI), special      272
Least generalization (LG) under relative implication (LGRI)      289
Least generalization (LG) under relative implication (LGRI) for Horn clauses      288
Least generalization (LG) under relative subsumption (LGRS)      285 286 355
Least generalization (LG) under relative subsumption (LGRS) for Horn clauses      287
Least generalization (LG) under subsumption (LGS)      251 254 265 274 279 286 355
Least generalization (LG) under subsumption (LGS) for Horn clauses      255
Least generalization (LG), summary of results      297
Least Herbrand model      112 129 213
Least Herbrand model as a concept      327n 333
Least Herbrand model, polynomial time algorithm for      342
Least upper bound (lub)      221 225
Lee, R.C.T.      ix 65 76 77n 90 91 93 94
Leiserson, C.      55 326 338n
Length (of a set of examples)      328
Length (of an example)      323
length parameter      324
Level-saturation method      88 124
Lewis, R.      358
lg      see "Least generalization"
LGGS      see "Least generalization under generalized subsumption"
LGI      see "Least generalization under implication"
LGI Algorithm      274
LGRI      see "Least generalization under relative implication"
LGRS      see "Least generalization under relative subsumption"
LGS      see "Least generalization under subsumption"
LGS Algorithm      255 274
Li, M.      338 353
Lifting lemma      82
Lifting Lemma for linear resolution      96
Lifting Lemma for SLD-resolution      109
Linear deduction      95 128
Linear derivation      94
Linear refutation      94
Linear resolution      xi 93 100
Linearly recursive clause      338
Ling, C.      197 300 334 357
Link-depth of a clause      336
Linked clause      336
LINUS      x 176 300
Literal      36
Lloyd, J.      ix 65 105 114n 118n 119 133 142 149 150 153 157n 158
Logic programming      ix xi 55 164 176 345 359
Logical consequence      10 11 30 49
Logical implication      12 see
Loveland, D.      90 94
Lower bound      221 225
Lub      see "Least upper bound"
Luckham, D.      94
Luebbe, M.      246n
Machine learning      ix 164 321 345
Maher, M.      65 359
Main tree in SLDNF-tree      133 140
Malicious noise      331
Marcinkowski, J.      125
Marriott, K.      65
Martelli, A.      65
Marvin      176 354
Matrix (in prenex form)      36
Matwin, S.      357
Maximal lower bound (mlb)      223 225
Maximal lower bound (mlb), complete set of      224
Maximal specialization (MS)      225
Maximal specialization (MS) under implication (MSI) for Horn clauses      276
maxsize      261
MDL      see "Minimum Description Length"
Mellish, C.      154 155
Membership query      330 331
Mendelson, E.      18
MG      see "Minimal generalization"
MGI      see "Minimal generalization under implication"
Mgu      see "Most general unifier"
Michalski, R.      354
Mill, J.S.      174
Minicozzi, E.      90 94 98
Minimal generalization (MG)      225
Minimal generalization (MG) under implication (MGI)      273
Minimal generalization (MG) under implication (MGI) for Horn clauses      267
Minimal upper bound (mub)      223 225
Minimal upper bound (mub), complete set of      224
Minimum description length      353 354n
Minsky, M.      164
MIS      176 192 300 354
Mitchell, T.      169 171
Mizoguchi, F.      358
Ml-smart      x
MLB      see "Maximal lower bound"
Mode declaration      339
Model      9 30 48
Model Inference Algorithm      193 306 354
Model inference problem      xii 168 176 179 184 299
modus ponens      57
Montanari, U.      65
Mooney, R.      358
MORAL      x 356
Morik, K.      x 356
Most general atom      233
Most general literal      305
Most general unifier (mgu)      63
ms      see "Maximal specialization"
mub      see "Minimal upper bound"
Muggleton, S.      x xii 76 164 176 197 220n 266 267 273 300 325n 336n 337—339 354 355 357—359
Multiple-predicate learning      170 354—357
Mutagenesis      358
n-step refinement      300
Name (in a representation)      327
Narayan, M.      197
Natarajan, B.      322 326 329 332
Necessary condition      11
Nedellec, C.      171 346 347
Negation      6
Negation as (finite) failure      128
Negative example      165 172 180 323
Negative literal      36
Neural network      164
newsize      261 311
Niblett, T.      261 285 311 355
Nienhuys-Cheng, S-H.      76 197 209 220 243 261 266 300 311 326 339
Nilsson, N.      90 92
Noise      xiii 170—172 177 346 352
Noise in PAC learning      331
Noise rate      332
Non-existence of solution for      168
Non-interactive learning      171 354—357
Non-recursive clause      277 335 347
Non-recursive program      335
Nonmonotonic problem setting      172 173 340
Nonmonotonic rule      128 141
Nonmonotonic setting      xi 177
Normal form      35
Normal goal      133
Normal problem setting      xi 167 168 280 327n 333
Normal program      130 135 166n
Norvig, P.      164
Notational conventions      15 33 53
Observational language ($\mathcal{C}_{0}$)      180
Occur check      64
Ohwada, H.      358
OL-resolution      94
OL-resolution, not refutation-complete      94n
One-step refinement      300
Optimal cover-refinement operator      316
Oracle      181 184 186 330
Ordered clause      246 307
Otsuki, S.      225n
Overly general (with respect to examples)      167
Overly specific (with respect to examples)      167
PAC algorithm      324
PAC learning      xiii 321 347
PAC learning in nonmonotonic ILP setting      340
PAC learning in normal ILP setting      333
PAC learning in propositional logic      332
PAC learning is worst case analysis      325
PAC learning under simple distributions      338
PAC predicting      330
Pacholski, L.      125
Page, C.D.      267 273 325n 334 337 358
Parent clauses      70
Partial order      220
Partial order, induced by quasi-order      221
Paterson, M.      65
Peirce, C.S.      173 174
Pettorossi, A.      207n
Pirnat, V.      358
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