Foundations of probabilistic logic programming: languages, semantics, inference and learning
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Gistrup
River Publishers
2023
New York ; Oxon Routledge 2023 |
| Ausgabe: | Second edition |
| Schriftenreihe: | River Publishers series in software engineering
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xli, 505 Seiten Diagramme |
| ISBN: | 9788770227193 9788770229586 |
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| adam_text | Contents xi Foreword xiii Preface to the 2nd Edition xv Preface Acknowledgments xix List of Figures xxi List of Tables xxvii List of Examples xxix List of Definitions xxxiii List of Theorems xxxvii List of Acronyms xxxix 1 Preliminaries 1.1 Orders, Lattices, Ordinals........................................................ 1.2 Mappings and Fixpoints........................................................... 1.3 Logic Programming.................................................................. 1.4 Semantics for Normal Logic Programs................................. 1.4.1 Program completion................................................. 1.4.2 Well-founded semantics........................................... 1.4.3 Stable model semantics........................................... 1.5 Probability Theory..................................................................... 1.6 Probabilistic Graphical Models.............................................. v 1 1 3 4 13 14 16 22 24 33
vi Contents 2 Probabilistic Logic Programming Languages 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 3 Semantics with Function Symbols 3.1 3.2 3.3 89 The Distribution Semantics for Programs with Function Sym bols ............................................................................... 91 Infinite Covering Set of Explanations..................................... 95 Comparison with Sato and Kameya’s Definition.................. 110 4 Hybrid Programs 4.1 4.2 4.3 4.4 43 Languages with the Distribution Semantics.......................... 43 2.1.1 Logic programswith annotated disjunctions ... 44 2.1.2 ProbLog...................................................................... 45 2.1.3 Probabilistic horn abduction.................................... 45 2.1.4 PRISM......................................................................... 46 The Distribution Semantics for Programs Without Function Symbols........................................................................ 47 Examples of Programs............................................................... 52 Equivalence of Expressive Power........................................... 58 Translation into Bayesian Networks........................................ 60 Generality of the Distribution Semantics.............................. 64 Extensions of the Distribution Semantics.............................. 66 CP-logic...................................................................................... 68 KBMC Probabilistic Logic Programming Languages ... 74 2.9.1 Bayesian logic programs
........................................ 74 2.9.2 CLP(BN)...................................................................... 74 2.9.3 The prolog factor language..................................... 77 Other Semantics for Probabilistic Logic Programming ... 79 2.10.1 Stochastic logic programs........................................ 79 2.10.2 ProPPR ...................................................................... 80 Other Semantics for Probabilistic Logics.............................. 82 2.11.1 Nilsson’s probabilistic logic..................................... 82 2.11.2 Markov logic networks............................................ 83 2.11.2.1 Encoding Markov logic networks with probabilistic logicprogramming ....83 2.11.3 Annotated probabilistic logic programs................. 86 Hybrid ProbLog......................................................................... Distributional Clauses............................................................... Extended PRISM..................................................................... cplint Hybrid Programs............................................................ 115 115 118 124 126
Contents vii Probabilistic Constraint Logic Programming........................ 4.5.1 Dealing with imprecise probability distributions............................................. 135 130 5 Semantics for Hybrid Programs with Function Symbols 5.1 Examples of PCLP with Function Symbols.......................... 5.2 Preliminaries.............................................................................. 5.3 The Semantics ofPCLP is Well-defined .............................. 145 145 147 155 6 Probabilistic Answer Set Programming 6.1 A Semantics for Unsound Programs .................................... 6.2 Features of Answer Set Programming.................................... 6.3 Probabilistic Answer Set Programming................................ 165 165 170 172 7 Complexity of Inference 7.1 Inference Tasks........................................................................ 7.2 Background on Complexity Theory....................................... 7.3 Complexity for Nonprobabilistic Inference.......................... 7.4 Complexity for Probabilistic Programs................................. 7.4.1 Complexity for acyclic and locally stratified pro grams ....................................................... 180 7.4.2 Complexity results from [Maud and Cozman, 2020].......................................................... 182 175 175 176 178 180 8 Exact Inference 8.1 PRISM......................................................................................... 8.2 Knowledge Compilation............................................................ 8.3 ProbLogl
.................................................................................. 8.4 cplint............................................................................................ 8.5 SLGAD..................................................................................... 8.6 PITA............................................................................................ 8.7 ProbLog2.................................................................................. 8.8 Tp Compilation........................................................................ 8.9 MPE and MAP ........................................................................ 8.9.1 MAP and MPE in probLog.................................... 8.9.2 MAP and MPE in PITA.......................................... 8.10 Modeling Assumptions in PITA.............................................. 8.10.1 PITA(OPT) ............................................................. 8.10.2 VIT with PITA.......................................................... 185 186 190 191 194 197 198 202 216 218 218 219 226 230 235 4.5
viii Contents Inference for Queries with an Infinite Number of Explanations.............................................................................. 235 Lifted Inference 9.1 Preliminaries on Lifted Inference........................................... 9.1.1 Variable elimination.................................................. 9.1.2 GC-FOVE................................................................... 9.2 LP2................................................................................................ 9.2.1 Translating probLog into PFL................................. 9.3 Lifted Inference with AggregationParfactors......................... 9.4 Weighted First-order Model Counting ................................. 9.5 Cyclic Logic Programs ........................................................... 9.6 Comparison of the Approaches............................................... 237 237 239 243 244 244 247 249 252 252 8.11 9 10 Approximate Inference 255 10.1 ProbLogl .................................................................................. 255 10.1.1 Iterative deepening..................................................... 255 10.1.2 fc-best ......................................................................... 257 10.1.3 Montecarlo............................................................... 258 10.2 MCINTYRE............................................................................... 260 10.3 Approximate Inference for Queries with an Infinite Number of Explanations........................................................................ 263 10.4
Conditional Approximate Inference........................................ 264 10.5 fc-optimal ................................................................................... 266 10.6 Explanation-based Approximate Weighted Model Counting 268 10.7 Approximate Inference with Tp-compilation........................ 270 11 Non-standard Inference 273 11.1 Possibilistic Logic Programming........................................... 273 11.2 Decision-theoretic ProbLog..................................................... 275 11.3 Algebraic ProbLog .................................................................. 284 12 Inference for Hybrid Programs 293 12.1 Inference for Extended PRISM.............................................. 293 12.2 Inference with Weighted Model Integration ........................ 300 12.2.1 Weighted Model Integration.................................... 300 12.2.2 Algebraic Model Counting.................................... 302 12.2.2.1 The probability density semiring andWMI............................... 304
Contents ix 12.2.2.2 Symbo .................................................... 305 12.2.2.3 Sampo....................................................... 307 12.3 Approximate Inference by Sampling for Hybrid Programs.................................................................................... 309 12.4 Approximate Inference with Bounded Error for Hybrid Pro grams ........................................................................... 311 12.5 Approximate Inference for the DISTR and EXP Tasks ... 314 13 Parameter Learning 13.1 PRISM Parameter Learning..................................................... 13.2 LLPAD and ALLPAD Parameter Learning........................... 13.3 LeProbLog.................................................................................. 13.4 EMBLEM.................................................................................. 13.5 ProbLog2 Parameter Learning.................................................. 13.6 Parameter Learning for Hybrid Programs.............................. 13.7 DeepProbLog............................................................................ 13.7.1 DeepProbLog inference........................................... 13.7.2 Learning in DeepProbLog........................................ 319 319 326 328 332 342 343 344 346 347 14 Structure Learning 14.1 Inductive Logic Programming.................................................. 14.2 LLPAD and ALLPAD Structure Learning ........................... 14.3 ProbLog Theory Compression ............................................... 14.4 ProbFOIL and
ProbFOIL+..................................................... 14.5 SLIPCOVER............................................................................... 14.5.1 The language bias.................................................... 14.5.2 Description of the algorithm.................................... 14.5.2.1 Function InitialBeams.......................... 14.5.2.2 Beam search with clause refinements . 14.5.3 Execution Example................................................. 14.6 Learning the Structure of Hybrid Programs........................... 14.7 Scaling PILP............................................................................... 14.7.1 LIFTCOVER .......................................................... 14.7.1.1 Liftable PLP .......................................... 14.7.1.2 Parameter learning................................. 14.7.1.3 Structure learning.................................... 14.7.2 SLEAHP.................................................................... 14.7.2.1 Hierarchical probabilistic logic programs............................... 351 351 354 357 358 364 364 364 366 368 369 372 378 378 379 381 386 389 389
x Contents 14.7.2.2 Parameter learning................................. 14.7.2.3 Structure learning.................................... Examples of Datasets............................................................. 397 409 416 15 cplint Examples 15.1 cplint Commands................................................................... 15.2 Natural Language Processing................................................ 15.2.1 Probabilistic context-free grammars....................... 15.2.2 Probabilistic left corner grammars ....................... 15.2.3 Hidden Markov models.......................................... 15.3 Drawing Binary Decision Diagrams...................................... 15.4 Gaussian Processes................................................................ 15.5 Dirichlet Processes ................................................................ 15.5.1 The stick-breaking process .................................... 15.5.2 The Chinese restaurant process.............................. 15.5.3 Mixture model........................................................... 15.6 Bayesian Estimation................................................................ 15.7 Kalman Filter.......................................................................... 15.8 Stochastic Logic Programs................................................... 15.9 Tile Map Generation................................................................ 15.10 Markov Logic Networks......................................................... 15.11
Truel........................................................................................... 15.12 Coupon Collector Problem................................................... 15.13 One-dimensional Random Walk............................................. 15.14 Latent Dirichlet Allocation................................................... 15.15 The Indian GPA Problem...................................................... 15.16 Bongard Problems.................................................................... 417 417 421 421 422 423 425 426 430 431 434 436 437 439 442 444 446 447 451 454 455 459 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 14.8
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| adam_txt |
Contents xi Foreword xiii Preface to the 2nd Edition xv Preface Acknowledgments xix List of Figures xxi List of Tables xxvii List of Examples xxix List of Definitions xxxiii List of Theorems xxxvii List of Acronyms xxxix 1 Preliminaries 1.1 Orders, Lattices, Ordinals. 1.2 Mappings and Fixpoints. 1.3 Logic Programming. 1.4 Semantics for Normal Logic Programs. 1.4.1 Program completion. 1.4.2 Well-founded semantics. 1.4.3 Stable model semantics. 1.5 Probability Theory. 1.6 Probabilistic Graphical Models. v 1 1 3 4 13 14 16 22 24 33
vi Contents 2 Probabilistic Logic Programming Languages 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 3 Semantics with Function Symbols 3.1 3.2 3.3 89 The Distribution Semantics for Programs with Function Sym bols . 91 Infinite Covering Set of Explanations. 95 Comparison with Sato and Kameya’s Definition. 110 4 Hybrid Programs 4.1 4.2 4.3 4.4 43 Languages with the Distribution Semantics. 43 2.1.1 Logic programswith annotated disjunctions . 44 2.1.2 ProbLog. 45 2.1.3 Probabilistic horn abduction. 45 2.1.4 PRISM. 46 The Distribution Semantics for Programs Without Function Symbols. 47 Examples of Programs. 52 Equivalence of Expressive Power. 58 Translation into Bayesian Networks. 60 Generality of the Distribution Semantics. 64 Extensions of the Distribution Semantics. 66 CP-logic. 68 KBMC Probabilistic Logic Programming Languages . 74 2.9.1 Bayesian logic programs
. 74 2.9.2 CLP(BN). 74 2.9.3 The prolog factor language. 77 Other Semantics for Probabilistic Logic Programming . 79 2.10.1 Stochastic logic programs. 79 2.10.2 ProPPR . 80 Other Semantics for Probabilistic Logics. 82 2.11.1 Nilsson’s probabilistic logic. 82 2.11.2 Markov logic networks. 83 2.11.2.1 Encoding Markov logic networks with probabilistic logicprogramming .83 2.11.3 Annotated probabilistic logic programs. 86 Hybrid ProbLog. Distributional Clauses. Extended PRISM. cplint Hybrid Programs. 115 115 118 124 126
Contents vii Probabilistic Constraint Logic Programming. 4.5.1 Dealing with imprecise probability distributions. 135 130 5 Semantics for Hybrid Programs with Function Symbols 5.1 Examples of PCLP with Function Symbols. 5.2 Preliminaries. 5.3 The Semantics ofPCLP is Well-defined . 145 145 147 155 6 Probabilistic Answer Set Programming 6.1 A Semantics for Unsound Programs . 6.2 Features of Answer Set Programming. 6.3 Probabilistic Answer Set Programming. 165 165 170 172 7 Complexity of Inference 7.1 Inference Tasks. 7.2 Background on Complexity Theory. 7.3 Complexity for Nonprobabilistic Inference. 7.4 Complexity for Probabilistic Programs. 7.4.1 Complexity for acyclic and locally stratified pro grams . 180 7.4.2 Complexity results from [Maud and Cozman, 2020]. 182 175 175 176 178 180 8 Exact Inference 8.1 PRISM. 8.2 Knowledge Compilation. 8.3 ProbLogl
. 8.4 cplint. 8.5 SLGAD. 8.6 PITA. 8.7 ProbLog2. 8.8 Tp Compilation. 8.9 MPE and MAP . 8.9.1 MAP and MPE in probLog. 8.9.2 MAP and MPE in PITA. 8.10 Modeling Assumptions in PITA. 8.10.1 PITA(OPT) . 8.10.2 VIT with PITA. 185 186 190 191 194 197 198 202 216 218 218 219 226 230 235 4.5
viii Contents Inference for Queries with an Infinite Number of Explanations. 235 Lifted Inference 9.1 Preliminaries on Lifted Inference. 9.1.1 Variable elimination. 9.1.2 GC-FOVE. 9.2 LP2. 9.2.1 Translating probLog into PFL. 9.3 Lifted Inference with AggregationParfactors. 9.4 Weighted First-order Model Counting . 9.5 Cyclic Logic Programs . 9.6 Comparison of the Approaches. 237 237 239 243 244 244 247 249 252 252 8.11 9 10 Approximate Inference 255 10.1 ProbLogl . 255 10.1.1 Iterative deepening. 255 10.1.2 fc-best . 257 10.1.3 Montecarlo. 258 10.2 MCINTYRE. 260 10.3 Approximate Inference for Queries with an Infinite Number of Explanations. 263 10.4
Conditional Approximate Inference. 264 10.5 fc-optimal . 266 10.6 Explanation-based Approximate Weighted Model Counting 268 10.7 Approximate Inference with Tp-compilation. 270 11 Non-standard Inference 273 11.1 Possibilistic Logic Programming. 273 11.2 Decision-theoretic ProbLog. 275 11.3 Algebraic ProbLog . 284 12 Inference for Hybrid Programs 293 12.1 Inference for Extended PRISM. 293 12.2 Inference with Weighted Model Integration . 300 12.2.1 Weighted Model Integration. 300 12.2.2 Algebraic Model Counting. 302 12.2.2.1 The probability density semiring andWMI. 304
Contents ix 12.2.2.2 Symbo . 305 12.2.2.3 Sampo. 307 12.3 Approximate Inference by Sampling for Hybrid Programs. 309 12.4 Approximate Inference with Bounded Error for Hybrid Pro grams . 311 12.5 Approximate Inference for the DISTR and EXP Tasks . 314 13 Parameter Learning 13.1 PRISM Parameter Learning. 13.2 LLPAD and ALLPAD Parameter Learning. 13.3 LeProbLog. 13.4 EMBLEM. 13.5 ProbLog2 Parameter Learning. 13.6 Parameter Learning for Hybrid Programs. 13.7 DeepProbLog. 13.7.1 DeepProbLog inference. 13.7.2 Learning in DeepProbLog. 319 319 326 328 332 342 343 344 346 347 14 Structure Learning 14.1 Inductive Logic Programming. 14.2 LLPAD and ALLPAD Structure Learning . 14.3 ProbLog Theory Compression . 14.4 ProbFOIL and
ProbFOIL+. 14.5 SLIPCOVER. 14.5.1 The language bias. 14.5.2 Description of the algorithm. 14.5.2.1 Function InitialBeams. 14.5.2.2 Beam search with clause refinements . 14.5.3 Execution Example. 14.6 Learning the Structure of Hybrid Programs. 14.7 Scaling PILP. 14.7.1 LIFTCOVER . 14.7.1.1 Liftable PLP . 14.7.1.2 Parameter learning. 14.7.1.3 Structure learning. 14.7.2 SLEAHP. 14.7.2.1 Hierarchical probabilistic logic programs. 351 351 354 357 358 364 364 364 366 368 369 372 378 378 379 381 386 389 389
x Contents 14.7.2.2 Parameter learning. 14.7.2.3 Structure learning. Examples of Datasets. 397 409 416 15 cplint Examples 15.1 cplint Commands. 15.2 Natural Language Processing. 15.2.1 Probabilistic context-free grammars. 15.2.2 Probabilistic left corner grammars . 15.2.3 Hidden Markov models. 15.3 Drawing Binary Decision Diagrams. 15.4 Gaussian Processes. 15.5 Dirichlet Processes . 15.5.1 The stick-breaking process . 15.5.2 The Chinese restaurant process. 15.5.3 Mixture model. 15.6 Bayesian Estimation. 15.7 Kalman Filter. 15.8 Stochastic Logic Programs. 15.9 Tile Map Generation. 15.10 Markov Logic Networks. 15.11
Truel. 15.12 Coupon Collector Problem. 15.13 One-dimensional Random Walk. 15.14 Latent Dirichlet Allocation. 15.15 The Indian GPA Problem. 15.16 Bongard Problems. 417 417 421 421 422 423 425 426 430 431 434 436 437 439 442 444 446 447 451 454 455 459 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 14.8 |
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| index_date | 2024-07-03T22:44:59Z |
| indexdate | 2024-07-10T10:01:42Z |
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| spelling | Riguzzi, Fabrizio Verfasser (DE-588)1036574067 aut Foundations of probabilistic logic programming languages, semantics, inference and learning Fabrizio Riguzzi Second edition Gistrup River Publishers 2023 New York ; Oxon Routledge 2023 xli, 505 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier River Publishers series in software engineering Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 s Stochastische Optimierung (DE-588)4057625-5 s DE-604 Erscheint auch als Online-Ausgabe 978-10-0092-321-6 Erscheint auch als Online-Ausgabe,ebook master 978-1-003-42742-1 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034590527&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Riguzzi, Fabrizio Foundations of probabilistic logic programming languages, semantics, inference and learning Stochastische Optimierung (DE-588)4057625-5 gnd Logische Programmierung (DE-588)4195096-3 gnd |
| subject_GND | (DE-588)4057625-5 (DE-588)4195096-3 |
| title | Foundations of probabilistic logic programming languages, semantics, inference and learning |
| title_auth | Foundations of probabilistic logic programming languages, semantics, inference and learning |
| title_exact_search | Foundations of probabilistic logic programming languages, semantics, inference and learning |
| title_exact_search_txtP | Foundations of probabilistic logic programming languages, semantics, inference and learning |
| title_full | Foundations of probabilistic logic programming languages, semantics, inference and learning Fabrizio Riguzzi |
| title_fullStr | Foundations of probabilistic logic programming languages, semantics, inference and learning Fabrizio Riguzzi |
| title_full_unstemmed | Foundations of probabilistic logic programming languages, semantics, inference and learning Fabrizio Riguzzi |
| title_short | Foundations of probabilistic logic programming |
| title_sort | foundations of probabilistic logic programming languages semantics inference and learning |
| title_sub | languages, semantics, inference and learning |
| topic | Stochastische Optimierung (DE-588)4057625-5 gnd Logische Programmierung (DE-588)4195096-3 gnd |
| topic_facet | Stochastische Optimierung Logische Programmierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034590527&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT riguzzifabrizio foundationsofprobabilisticlogicprogramminglanguagessemanticsinferenceandlearning |