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Nienhuys-Cheng S., Wolf R. — Foundations of Inductive Logic Programming
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Название: Foundations of Inductive Logic ProgrammingАвторы: 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: Рубрика: Computer science /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Издание: 1Год издания: 1997Количество страниц: 410Добавлена в каталог: 13.12.2013Операции: Положить на полку |
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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 ( 106 Horn, A. 105 Horvath, T. x 338 339 Hume, D. (philosopher) 174 Hume, D. (researcher) 197 Hypothesis language ( 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 ( 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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