Foundations of probabilistic logic programming: languages, semantics, inference and learning
Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programmin...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Gistrup, Denmark
River Publishers
[2022]
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| Ausgabe: | Second edition |
| Schriftenreihe: | River Publishers series in software engineering
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| Online-Zugang: | DE-573 Volltext Taylor & Francis |
| Zusammenfassung: | Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming. Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of probabilistic logic programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the distribution semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols is placed on a sound footing |
| Beschreibung: | 1 Online-Ressource (xli, 505 Seiten) |
| ISBN: | 9788770227063 8770227063 9781003427421 1003427421 9781000923223 1000923223 9781000923216 1000923215 |
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| 245 | 1 | 0 | |a Foundations of probabilistic logic programming |b languages, semantics, inference and learning |c Fabrizio Riguzzi |
| 250 | |a Second edition | ||
| 264 | 1 | |a Gistrup, Denmark |b River Publishers |c [2022] | |
| 300 | |a 1 Online-Ressource (xli, 505 Seiten) | ||
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| 490 | 0 | |a River Publishers series in software engineering | |
| 505 | 8 | |a Foreword xi Preface to the 2nd Edition xiii Preface xv 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 1.1 Orders, Lattices, | |
| 505 | 8 | |a Ordinals 1 1.2 Mappings and Fixpoints 3 1.3 Logic Programming 4 1.4 Semantics for Normal Logic Programs 13 1.4.1 Program completion 14 1.4.2 Well-founded semantics 16 1.4.3 Stable model semantics 22 1.5 Probability Theory 24 1.6 Probabilistic Graphical Models 33 2 Probabilistic Logic Programming Languages 43 2.1 Languages with the Distribution Semantics 43 2.1.1 Logic programs with annotated disjunctions 44 2.1.2 ProbLog 45 2.1.3 Probabilistic horn abduction 45 2.1.4 PRISM 46 2.2 The Distribution Semantics for Programs Without Function Symbols 47 2.3 Examples of Programs 52 2.4 Equivalence of Expressive Power 58 2.5 Translation into Bayesian Networks 60 2.6 Generality of the Distribution Semantics 64 2.7 Extensions of the Distribution Semantics 66 2.8 CP-logic 68 2.9 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 2.10 Other Semantics for Probabilistic Logic Programming 79 2.10.1 Stochastic logic | |
| 505 | 8 | |a programs 79 2.10.2 ProPPR 80 2.11 Other Semantics for Probabilistic Logics 82 2.11.1 Nilsson⁰́₉s probabilistic logic 82 2.11.2 Markov logic networks 83 2.11.2.1 Encoding Markov logic networks with probabilistic logic programming 83 2.11.3 Annotated probabilistic logic programs 86 3 Semantics with Function Symbols 89 3.1 The Distribution Semantics for Programs with Function Symbols 91 3.2 Infinite Covering Set of Explanations 95 3.3 Comparison with Sato and Kameya⁰́₉s Definition 110 4 Hybrid Programs 115 4.1 Hybrid ProbLog 115 4.2 Distributional Clauses 118 4.3 Extended PRISM 124 4.4 cplint Hybrid Programs 126 4.5 Probabilistic Constraint Logic Programming 130 4.5.1 Dealing with imprecise probability distributions 135 5 Semantics for Hybrid Programs with Function Symbols 145 5.1 Examples of PCLP with Function Symbols 145 5.2 Preliminaries 147 5.3 The Semantics of PCLP is Well-defined 155 6 Probabilistic Answer Set Programming 165 6.1 A Semantics for Unsound Programs | |
| 505 | 8 | |a 165 6.2 Features of Answer Set Programming 170 6.3 Probabilistic Answer Set Programming 172 7 Complexity of Inference 175 7.1 Inference Tasks 175 7.2 Background on Complexity Theory 176 7.3 Complexity for Nonprobabilistic Inference 178 7.4 Complexity for Probabilistic Programs 180 7.4.1 Complexity for acyclic and locally stratified programs 180 7.4.2 Complexity results from [Mau©Ł and Cozman, | |
| 505 | 8 | |a 2020] 182 8 Exact Inference 185 8.1 PRISM 186 8.2 Knowledge Compilation 190 8.3 ProbLog1 191 8.4 cplint 194 8.5 SLGAD 197 8.6 PITA 198 8.7 ProbLog2 202 8.8 TP Compilation 216 8.9 MPEandMAP 218 8.9.1 MAP and MPE in probLog 218 8.9.2 MAP and MPE in PITA 219 8.10 Modeling Assumptions in PITA 226 8.10.1 PITA(OPT) 230 8.10.2 VIT with PITA 235 8.11 Inference for Queries with an Infinite Number of Explanations 235 9 Lifted Inference 237 9.1 Preliminaries on Lifted Inference 237 9.1.1 Variable elimination 239 9.1.2 GC-FOVE 243 9.2 LP2 244 9.2.1 Translating probLog into PFL 244 9.3 Lifted Inference with Aggregation Parfactors 247 9.4 Weighted First-order Model Counting 249 9.5 Cyclic Logic Programs 252 9.6 Comparison of the Approaches 252 10 Approximate Inference 255 10.1 ProbLog1 255 10.1.1 Iterative deepening 255 10.1.2 k-best 257 10.1.3 Monte carlo 258 10.2 MCINTYRE 260 10.3 Approximate Inference for Queries with an Infinite Number of Explanations 263 10.4 Conditional Approximate Inference | |
| 505 | 8 | |a 264 10.5 k-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 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 Programs 311 12.5 Approximate Inference for the DISTR and EXP Tasks 314 13 Parameter Learning 319 13.1 PRISM Parameter Learning 319 13.2 LLPAD and ALLPAD Parameter Learning 326 13.3 LeProbLog 328 13.4 EMBLEM 332 13.5 ProbLog2 Parameter Learning 342 13.6 Parameter Learning for Hybrid Programs 343 13.7 DeepProbLog | |
| 505 | 8 | |a 344 13.7.1 DeepProbLog inference 346 13.7.2 Learning in DeepProbLog 347 14 Structure Learning 351 14.1 Inductive Logic Programming 351 14.2 LLPAD and ALLPAD Structure Learning 354 14.3 ProbLog Theory Compression 357 14.4 ProbFOIL and ProbFOIL+ 358 14.5 SLIPCOVER 364 14.5.1 The language bias 364 14.5.2 Description of the algorithm 364 14.5.2.1 Function INITIALBEAMS 366 14.5.2.2 Beam search with clause refinements 368 14.5.3 Execution Example 369 14.6 Learning the Structure of Hybrid Programs 372 14.7 Scaling PILP 378 14.7.1 LIFTCOVER 378 14.7.1.1 Liftable PLP 379 14.7.1.2 Parameter learning 381 14.7.1.3 Structure learning 386 14.7.2 SLEAHP 389 14.7.2.1 Hierarchical probabilistic logic programs 389 14.7.2.2 Parameter learning 397 14.7.2.3 Structure learning 409 14.8 Examples of Datasets 416 15 cplint Examples 417 15.1 cplint Commands 417 15.2 Natural Language Processing 421 15.2.1 Probabilistic context-free grammars 421 15.2.2 Probabilistic left corner grammars 422 15.2.3 Hidden Markov | |
| 505 | 8 | |a models 423 15.3 Drawing Binary Decision Diagrams 425 15.4 Gaussian Processes 426 15.5 Dirichlet Processes 430 15.5.1 The stick-breaking process 431 15.5.2 The Chinese restaurant process 434 15.5.3 Mixture model 436 15.6 Bayesian Estimation 437 15.7 Kalman Filter 439 15.8 Stochastic Logic Programs 442 15.9 Tile Map Generation 444 15.10 Markov Logic Networks 446 15.11 Truel 447 15.12 Coupon Collector Problem 451 15.13 One-dimensional Random Walk 454 15.14 Latent Dirichlet Allocation 455 15.15 The Indian GPA Problem 459 15.16 Bongard Problems 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 | |
| 520 | 3 | |a Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming. Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of probabilistic logic programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the distribution semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols is placed on a sound footing | |
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| 653 | 0 | |a COMPUTERS / Programming / Software Development | |
| 653 | 0 | |a COMPUTERS / Programming / General | |
| 653 | 0 | |a MATHEMATICS / Probability & Statistics / General | |
| 653 | 0 | |a Logic programming | |
| 653 | 0 | |a Probabilities / Data processing | |
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Datensatz im Suchindex
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| author | Riguzzi, Fabrizio |
| author_GND | (DE-588)1036574067 |
| author_facet | Riguzzi, Fabrizio |
| author_role | aut |
| author_sort | Riguzzi, Fabrizio |
| author_variant | f r fr |
| building | Verbundindex |
| bvnumber | BV049485431 |
| classification_rvk | ST 230 |
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| contents | Foreword xi Preface to the 2nd Edition xiii Preface xv 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 1.1 Orders, Lattices, Ordinals 1 1.2 Mappings and Fixpoints 3 1.3 Logic Programming 4 1.4 Semantics for Normal Logic Programs 13 1.4.1 Program completion 14 1.4.2 Well-founded semantics 16 1.4.3 Stable model semantics 22 1.5 Probability Theory 24 1.6 Probabilistic Graphical Models 33 2 Probabilistic Logic Programming Languages 43 2.1 Languages with the Distribution Semantics 43 2.1.1 Logic programs with annotated disjunctions 44 2.1.2 ProbLog 45 2.1.3 Probabilistic horn abduction 45 2.1.4 PRISM 46 2.2 The Distribution Semantics for Programs Without Function Symbols 47 2.3 Examples of Programs 52 2.4 Equivalence of Expressive Power 58 2.5 Translation into Bayesian Networks 60 2.6 Generality of the Distribution Semantics 64 2.7 Extensions of the Distribution Semantics 66 2.8 CP-logic 68 2.9 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 2.10 Other Semantics for Probabilistic Logic Programming 79 2.10.1 Stochastic logic programs 79 2.10.2 ProPPR 80 2.11 Other Semantics for Probabilistic Logics 82 2.11.1 Nilsson⁰́₉s probabilistic logic 82 2.11.2 Markov logic networks 83 2.11.2.1 Encoding Markov logic networks with probabilistic logic programming 83 2.11.3 Annotated probabilistic logic programs 86 3 Semantics with Function Symbols 89 3.1 The Distribution Semantics for Programs with Function Symbols 91 3.2 Infinite Covering Set of Explanations 95 3.3 Comparison with Sato and Kameya⁰́₉s Definition 110 4 Hybrid Programs 115 4.1 Hybrid ProbLog 115 4.2 Distributional Clauses 118 4.3 Extended PRISM 124 4.4 cplint Hybrid Programs 126 4.5 Probabilistic Constraint Logic Programming 130 4.5.1 Dealing with imprecise probability distributions 135 5 Semantics for Hybrid Programs with Function Symbols 145 5.1 Examples of PCLP with Function Symbols 145 5.2 Preliminaries 147 5.3 The Semantics of PCLP is Well-defined 155 6 Probabilistic Answer Set Programming 165 6.1 A Semantics for Unsound Programs 165 6.2 Features of Answer Set Programming 170 6.3 Probabilistic Answer Set Programming 172 7 Complexity of Inference 175 7.1 Inference Tasks 175 7.2 Background on Complexity Theory 176 7.3 Complexity for Nonprobabilistic Inference 178 7.4 Complexity for Probabilistic Programs 180 7.4.1 Complexity for acyclic and locally stratified programs 180 7.4.2 Complexity results from [Mau©Ł and Cozman, 2020] 182 8 Exact Inference 185 8.1 PRISM 186 8.2 Knowledge Compilation 190 8.3 ProbLog1 191 8.4 cplint 194 8.5 SLGAD 197 8.6 PITA 198 8.7 ProbLog2 202 8.8 TP Compilation 216 8.9 MPEandMAP 218 8.9.1 MAP and MPE in probLog 218 8.9.2 MAP and MPE in PITA 219 8.10 Modeling Assumptions in PITA 226 8.10.1 PITA(OPT) 230 8.10.2 VIT with PITA 235 8.11 Inference for Queries with an Infinite Number of Explanations 235 9 Lifted Inference 237 9.1 Preliminaries on Lifted Inference 237 9.1.1 Variable elimination 239 9.1.2 GC-FOVE 243 9.2 LP2 244 9.2.1 Translating probLog into PFL 244 9.3 Lifted Inference with Aggregation Parfactors 247 9.4 Weighted First-order Model Counting 249 9.5 Cyclic Logic Programs 252 9.6 Comparison of the Approaches 252 10 Approximate Inference 255 10.1 ProbLog1 255 10.1.1 Iterative deepening 255 10.1.2 k-best 257 10.1.3 Monte carlo 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 k-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 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 Programs 311 12.5 Approximate Inference for the DISTR and EXP Tasks 314 13 Parameter Learning 319 13.1 PRISM Parameter Learning 319 13.2 LLPAD and ALLPAD Parameter Learning 326 13.3 LeProbLog 328 13.4 EMBLEM 332 13.5 ProbLog2 Parameter Learning 342 13.6 Parameter Learning for Hybrid Programs 343 13.7 DeepProbLog 344 13.7.1 DeepProbLog inference 346 13.7.2 Learning in DeepProbLog 347 14 Structure Learning 351 14.1 Inductive Logic Programming 351 14.2 LLPAD and ALLPAD Structure Learning 354 14.3 ProbLog Theory Compression 357 14.4 ProbFOIL and ProbFOIL+ 358 14.5 SLIPCOVER 364 14.5.1 The language bias 364 14.5.2 Description of the algorithm 364 14.5.2.1 Function INITIALBEAMS 366 14.5.2.2 Beam search with clause refinements 368 14.5.3 Execution Example 369 14.6 Learning the Structure of Hybrid Programs 372 14.7 Scaling PILP 378 14.7.1 LIFTCOVER 378 14.7.1.1 Liftable PLP 379 14.7.1.2 Parameter learning 381 14.7.1.3 Structure learning 386 14.7.2 SLEAHP 389 14.7.2.1 Hierarchical probabilistic logic programs 389 14.7.2.2 Parameter learning 397 14.7.2.3 Structure learning 409 14.8 Examples of Datasets 416 15 cplint Examples 417 15.1 cplint Commands 417 15.2 Natural Language Processing 421 15.2.1 Probabilistic context-free grammars 421 15.2.2 Probabilistic left corner grammars 422 15.2.3 Hidden Markov models 423 15.3 Drawing Binary Decision Diagrams 425 15.4 Gaussian Processes 426 15.5 Dirichlet Processes 430 15.5.1 The stick-breaking process 431 15.5.2 The Chinese restaurant process 434 15.5.3 Mixture model 436 15.6 Bayesian Estimation 437 15.7 Kalman Filter 439 15.8 Stochastic Logic Programs 442 15.9 Tile Map Generation 444 15.10 Markov Logic Networks 446 15.11 Truel 447 15.12 Coupon Collector Problem 451 15.13 One-dimensional Random Walk 454 15.14 Latent Dirichlet Allocation 455 15.15 The Indian GPA Problem 459 15.16 Bongard Problems 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 |
| ctrlnum | (OCoLC)1418690430 (DE-599)BVBBV049485431 |
| discipline | Informatik |
| edition | Second edition |
| format | Electronic eBook |
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77 2.10 Other Semantics for Probabilistic Logic Programming 79 2.10.1 Stochastic logic</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">programs 79 2.10.2 ProPPR 80 2.11 Other Semantics for Probabilistic Logics 82 2.11.1 Nilsson⁰́₉s probabilistic logic 82 2.11.2 Markov logic networks 83 2.11.2.1 Encoding Markov logic networks with probabilistic logic programming 83 2.11.3 Annotated probabilistic logic programs 86 3 Semantics with Function Symbols 89 3.1 The Distribution Semantics for Programs with Function Symbols 91 3.2 Infinite Covering Set of Explanations 95 3.3 Comparison with Sato and Kameya⁰́₉s Definition 110 4 Hybrid Programs 115 4.1 Hybrid ProbLog 115 4.2 Distributional Clauses 118 4.3 Extended PRISM 124 4.4 cplint Hybrid Programs 126 4.5 Probabilistic Constraint Logic Programming 130 4.5.1 Dealing with imprecise probability distributions 135 5 Semantics for Hybrid Programs with Function Symbols 145 5.1 Examples of PCLP with Function Symbols 145 5.2 Preliminaries 147 5.3 The Semantics of PCLP is Well-defined 155 6 Probabilistic Answer Set Programming 165 6.1 A Semantics for Unsound Programs</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">165 6.2 Features of Answer Set Programming 170 6.3 Probabilistic Answer Set Programming 172 7 Complexity of Inference 175 7.1 Inference Tasks 175 7.2 Background on Complexity Theory 176 7.3 Complexity for Nonprobabilistic Inference 178 7.4 Complexity for Probabilistic Programs 180 7.4.1 Complexity for acyclic and locally stratified programs 180 7.4.2 Complexity results from [Mau©Ł and Cozman,</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2020] 182 8 Exact Inference 185 8.1 PRISM 186 8.2 Knowledge Compilation 190 8.3 ProbLog1 191 8.4 cplint 194 8.5 SLGAD 197 8.6 PITA 198 8.7 ProbLog2 202 8.8 TP Compilation 216 8.9 MPEandMAP 218 8.9.1 MAP and MPE in probLog 218 8.9.2 MAP and MPE in PITA 219 8.10 Modeling Assumptions in PITA 226 8.10.1 PITA(OPT) 230 8.10.2 VIT with PITA 235 8.11 Inference for Queries with an Infinite Number of Explanations 235 9 Lifted Inference 237 9.1 Preliminaries on Lifted Inference 237 9.1.1 Variable elimination 239 9.1.2 GC-FOVE 243 9.2 LP2 244 9.2.1 Translating probLog into PFL 244 9.3 Lifted Inference with Aggregation Parfactors 247 9.4 Weighted First-order Model Counting 249 9.5 Cyclic Logic Programs 252 9.6 Comparison of the Approaches 252 10 Approximate Inference 255 10.1 ProbLog1 255 10.1.1 Iterative deepening 255 10.1.2 k-best 257 10.1.3 Monte carlo 258 10.2 MCINTYRE 260 10.3 Approximate Inference for Queries with an Infinite Number of Explanations 263 10.4 Conditional Approximate Inference</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">264 10.5 k-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 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 Programs 311 12.5 Approximate Inference for the DISTR and EXP Tasks 314 13 Parameter Learning 319 13.1 PRISM Parameter Learning 319 13.2 LLPAD and ALLPAD Parameter Learning 326 13.3 LeProbLog 328 13.4 EMBLEM 332 13.5 ProbLog2 Parameter Learning 342 13.6 Parameter Learning for Hybrid Programs 343 13.7 DeepProbLog</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">344 13.7.1 DeepProbLog inference 346 13.7.2 Learning in DeepProbLog 347 14 Structure Learning 351 14.1 Inductive Logic Programming 351 14.2 LLPAD and ALLPAD Structure Learning 354 14.3 ProbLog Theory Compression 357 14.4 ProbFOIL and ProbFOIL+ 358 14.5 SLIPCOVER 364 14.5.1 The language bias 364 14.5.2 Description of the algorithm 364 14.5.2.1 Function INITIALBEAMS 366 14.5.2.2 Beam search with clause refinements 368 14.5.3 Execution Example 369 14.6 Learning the Structure of Hybrid Programs 372 14.7 Scaling PILP 378 14.7.1 LIFTCOVER 378 14.7.1.1 Liftable PLP 379 14.7.1.2 Parameter learning 381 14.7.1.3 Structure learning 386 14.7.2 SLEAHP 389 14.7.2.1 Hierarchical probabilistic logic programs 389 14.7.2.2 Parameter learning 397 14.7.2.3 Structure learning 409 14.8 Examples of Datasets 416 15 cplint Examples 417 15.1 cplint Commands 417 15.2 Natural Language Processing 421 15.2.1 Probabilistic context-free grammars 421 15.2.2 Probabilistic left corner grammars 422 15.2.3 Hidden Markov</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">models 423 15.3 Drawing Binary Decision Diagrams 425 15.4 Gaussian Processes 426 15.5 Dirichlet Processes 430 15.5.1 The stick-breaking process 431 15.5.2 The Chinese restaurant process 434 15.5.3 Mixture model 436 15.6 Bayesian Estimation 437 15.7 Kalman Filter 439 15.8 Stochastic Logic Programs 442 15.9 Tile Map Generation 444 15.10 Markov Logic Networks 446 15.11 Truel 447 15.12 Coupon Collector Problem 451 15.13 One-dimensional Random Walk 454 15.14 Latent Dirichlet Allocation 455 15.15 The Indian GPA Problem 459 15.16 Bongard Problems 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming. Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of probabilistic logic programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the distribution semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. 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| id | DE-604.BV049485431 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:18:52Z |
| indexdate | 2024-07-20T04:08:41Z |
| institution | BVB |
| isbn | 9788770227063 8770227063 9781003427421 1003427421 9781000923223 1000923223 9781000923216 1000923215 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034830834 |
| oclc_num | 1418690430 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | 1 Online-Ressource (xli, 505 Seiten) |
| psigel | ZDB-37-RPEB |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | River Publishers |
| record_format | marc |
| series2 | River Publishers series in software engineering |
| spelling | Riguzzi, Fabrizio Verfasser (DE-588)1036574067 aut Foundations of probabilistic logic programming languages, semantics, inference and learning Fabrizio Riguzzi Second edition Gistrup, Denmark River Publishers [2022] 1 Online-Ressource (xli, 505 Seiten) txt rdacontent c rdamedia cr rdacarrier River Publishers series in software engineering Foreword xi Preface to the 2nd Edition xiii Preface xv 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 1.1 Orders, Lattices, Ordinals 1 1.2 Mappings and Fixpoints 3 1.3 Logic Programming 4 1.4 Semantics for Normal Logic Programs 13 1.4.1 Program completion 14 1.4.2 Well-founded semantics 16 1.4.3 Stable model semantics 22 1.5 Probability Theory 24 1.6 Probabilistic Graphical Models 33 2 Probabilistic Logic Programming Languages 43 2.1 Languages with the Distribution Semantics 43 2.1.1 Logic programs with annotated disjunctions 44 2.1.2 ProbLog 45 2.1.3 Probabilistic horn abduction 45 2.1.4 PRISM 46 2.2 The Distribution Semantics for Programs Without Function Symbols 47 2.3 Examples of Programs 52 2.4 Equivalence of Expressive Power 58 2.5 Translation into Bayesian Networks 60 2.6 Generality of the Distribution Semantics 64 2.7 Extensions of the Distribution Semantics 66 2.8 CP-logic 68 2.9 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 2.10 Other Semantics for Probabilistic Logic Programming 79 2.10.1 Stochastic logic programs 79 2.10.2 ProPPR 80 2.11 Other Semantics for Probabilistic Logics 82 2.11.1 Nilsson⁰́₉s probabilistic logic 82 2.11.2 Markov logic networks 83 2.11.2.1 Encoding Markov logic networks with probabilistic logic programming 83 2.11.3 Annotated probabilistic logic programs 86 3 Semantics with Function Symbols 89 3.1 The Distribution Semantics for Programs with Function Symbols 91 3.2 Infinite Covering Set of Explanations 95 3.3 Comparison with Sato and Kameya⁰́₉s Definition 110 4 Hybrid Programs 115 4.1 Hybrid ProbLog 115 4.2 Distributional Clauses 118 4.3 Extended PRISM 124 4.4 cplint Hybrid Programs 126 4.5 Probabilistic Constraint Logic Programming 130 4.5.1 Dealing with imprecise probability distributions 135 5 Semantics for Hybrid Programs with Function Symbols 145 5.1 Examples of PCLP with Function Symbols 145 5.2 Preliminaries 147 5.3 The Semantics of PCLP is Well-defined 155 6 Probabilistic Answer Set Programming 165 6.1 A Semantics for Unsound Programs 165 6.2 Features of Answer Set Programming 170 6.3 Probabilistic Answer Set Programming 172 7 Complexity of Inference 175 7.1 Inference Tasks 175 7.2 Background on Complexity Theory 176 7.3 Complexity for Nonprobabilistic Inference 178 7.4 Complexity for Probabilistic Programs 180 7.4.1 Complexity for acyclic and locally stratified programs 180 7.4.2 Complexity results from [Mau©Ł and Cozman, 2020] 182 8 Exact Inference 185 8.1 PRISM 186 8.2 Knowledge Compilation 190 8.3 ProbLog1 191 8.4 cplint 194 8.5 SLGAD 197 8.6 PITA 198 8.7 ProbLog2 202 8.8 TP Compilation 216 8.9 MPEandMAP 218 8.9.1 MAP and MPE in probLog 218 8.9.2 MAP and MPE in PITA 219 8.10 Modeling Assumptions in PITA 226 8.10.1 PITA(OPT) 230 8.10.2 VIT with PITA 235 8.11 Inference for Queries with an Infinite Number of Explanations 235 9 Lifted Inference 237 9.1 Preliminaries on Lifted Inference 237 9.1.1 Variable elimination 239 9.1.2 GC-FOVE 243 9.2 LP2 244 9.2.1 Translating probLog into PFL 244 9.3 Lifted Inference with Aggregation Parfactors 247 9.4 Weighted First-order Model Counting 249 9.5 Cyclic Logic Programs 252 9.6 Comparison of the Approaches 252 10 Approximate Inference 255 10.1 ProbLog1 255 10.1.1 Iterative deepening 255 10.1.2 k-best 257 10.1.3 Monte carlo 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 k-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 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 Programs 311 12.5 Approximate Inference for the DISTR and EXP Tasks 314 13 Parameter Learning 319 13.1 PRISM Parameter Learning 319 13.2 LLPAD and ALLPAD Parameter Learning 326 13.3 LeProbLog 328 13.4 EMBLEM 332 13.5 ProbLog2 Parameter Learning 342 13.6 Parameter Learning for Hybrid Programs 343 13.7 DeepProbLog 344 13.7.1 DeepProbLog inference 346 13.7.2 Learning in DeepProbLog 347 14 Structure Learning 351 14.1 Inductive Logic Programming 351 14.2 LLPAD and ALLPAD Structure Learning 354 14.3 ProbLog Theory Compression 357 14.4 ProbFOIL and ProbFOIL+ 358 14.5 SLIPCOVER 364 14.5.1 The language bias 364 14.5.2 Description of the algorithm 364 14.5.2.1 Function INITIALBEAMS 366 14.5.2.2 Beam search with clause refinements 368 14.5.3 Execution Example 369 14.6 Learning the Structure of Hybrid Programs 372 14.7 Scaling PILP 378 14.7.1 LIFTCOVER 378 14.7.1.1 Liftable PLP 379 14.7.1.2 Parameter learning 381 14.7.1.3 Structure learning 386 14.7.2 SLEAHP 389 14.7.2.1 Hierarchical probabilistic logic programs 389 14.7.2.2 Parameter learning 397 14.7.2.3 Structure learning 409 14.8 Examples of Datasets 416 15 cplint Examples 417 15.1 cplint Commands 417 15.2 Natural Language Processing 421 15.2.1 Probabilistic context-free grammars 421 15.2.2 Probabilistic left corner grammars 422 15.2.3 Hidden Markov models 423 15.3 Drawing Binary Decision Diagrams 425 15.4 Gaussian Processes 426 15.5 Dirichlet Processes 430 15.5.1 The stick-breaking process 431 15.5.2 The Chinese restaurant process 434 15.5.3 Mixture model 436 15.6 Bayesian Estimation 437 15.7 Kalman Filter 439 15.8 Stochastic Logic Programs 442 15.9 Tile Map Generation 444 15.10 Markov Logic Networks 446 15.11 Truel 447 15.12 Coupon Collector Problem 451 15.13 One-dimensional Random Walk 454 15.14 Latent Dirichlet Allocation 455 15.15 The Indian GPA Problem 459 15.16 Bongard Problems 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming. Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of probabilistic logic programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the distribution semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols is placed on a sound footing Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Logische Programmierung (DE-588)4195096-3 gnd rswk-swf Logic programming Probabilities / Data processing Programmation logique Probabilités / Informatique COMPUTERS / Programming / Software Development COMPUTERS / Programming / General MATHEMATICS / Probability & Statistics / General Logische Programmierung (DE-588)4195096-3 s Stochastische Optimierung (DE-588)4057625-5 s DE-604 Erscheint auch als Druck-Ausgabe 978-87-7022-719-3 https://ieeexplore.ieee.org/book/9999514 Aggregator URL des Erstveröffentlichers Volltext https://www.taylorfrancis.com/books/9781003427421 Taylor & Francis |
| spellingShingle | Riguzzi, Fabrizio Foundations of probabilistic logic programming languages, semantics, inference and learning Foreword xi Preface to the 2nd Edition xiii Preface xv 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 1.1 Orders, Lattices, Ordinals 1 1.2 Mappings and Fixpoints 3 1.3 Logic Programming 4 1.4 Semantics for Normal Logic Programs 13 1.4.1 Program completion 14 1.4.2 Well-founded semantics 16 1.4.3 Stable model semantics 22 1.5 Probability Theory 24 1.6 Probabilistic Graphical Models 33 2 Probabilistic Logic Programming Languages 43 2.1 Languages with the Distribution Semantics 43 2.1.1 Logic programs with annotated disjunctions 44 2.1.2 ProbLog 45 2.1.3 Probabilistic horn abduction 45 2.1.4 PRISM 46 2.2 The Distribution Semantics for Programs Without Function Symbols 47 2.3 Examples of Programs 52 2.4 Equivalence of Expressive Power 58 2.5 Translation into Bayesian Networks 60 2.6 Generality of the Distribution Semantics 64 2.7 Extensions of the Distribution Semantics 66 2.8 CP-logic 68 2.9 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 2.10 Other Semantics for Probabilistic Logic Programming 79 2.10.1 Stochastic logic programs 79 2.10.2 ProPPR 80 2.11 Other Semantics for Probabilistic Logics 82 2.11.1 Nilsson⁰́₉s probabilistic logic 82 2.11.2 Markov logic networks 83 2.11.2.1 Encoding Markov logic networks with probabilistic logic programming 83 2.11.3 Annotated probabilistic logic programs 86 3 Semantics with Function Symbols 89 3.1 The Distribution Semantics for Programs with Function Symbols 91 3.2 Infinite Covering Set of Explanations 95 3.3 Comparison with Sato and Kameya⁰́₉s Definition 110 4 Hybrid Programs 115 4.1 Hybrid ProbLog 115 4.2 Distributional Clauses 118 4.3 Extended PRISM 124 4.4 cplint Hybrid Programs 126 4.5 Probabilistic Constraint Logic Programming 130 4.5.1 Dealing with imprecise probability distributions 135 5 Semantics for Hybrid Programs with Function Symbols 145 5.1 Examples of PCLP with Function Symbols 145 5.2 Preliminaries 147 5.3 The Semantics of PCLP is Well-defined 155 6 Probabilistic Answer Set Programming 165 6.1 A Semantics for Unsound Programs 165 6.2 Features of Answer Set Programming 170 6.3 Probabilistic Answer Set Programming 172 7 Complexity of Inference 175 7.1 Inference Tasks 175 7.2 Background on Complexity Theory 176 7.3 Complexity for Nonprobabilistic Inference 178 7.4 Complexity for Probabilistic Programs 180 7.4.1 Complexity for acyclic and locally stratified programs 180 7.4.2 Complexity results from [Mau©Ł and Cozman, 2020] 182 8 Exact Inference 185 8.1 PRISM 186 8.2 Knowledge Compilation 190 8.3 ProbLog1 191 8.4 cplint 194 8.5 SLGAD 197 8.6 PITA 198 8.7 ProbLog2 202 8.8 TP Compilation 216 8.9 MPEandMAP 218 8.9.1 MAP and MPE in probLog 218 8.9.2 MAP and MPE in PITA 219 8.10 Modeling Assumptions in PITA 226 8.10.1 PITA(OPT) 230 8.10.2 VIT with PITA 235 8.11 Inference for Queries with an Infinite Number of Explanations 235 9 Lifted Inference 237 9.1 Preliminaries on Lifted Inference 237 9.1.1 Variable elimination 239 9.1.2 GC-FOVE 243 9.2 LP2 244 9.2.1 Translating probLog into PFL 244 9.3 Lifted Inference with Aggregation Parfactors 247 9.4 Weighted First-order Model Counting 249 9.5 Cyclic Logic Programs 252 9.6 Comparison of the Approaches 252 10 Approximate Inference 255 10.1 ProbLog1 255 10.1.1 Iterative deepening 255 10.1.2 k-best 257 10.1.3 Monte carlo 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 k-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 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 Programs 311 12.5 Approximate Inference for the DISTR and EXP Tasks 314 13 Parameter Learning 319 13.1 PRISM Parameter Learning 319 13.2 LLPAD and ALLPAD Parameter Learning 326 13.3 LeProbLog 328 13.4 EMBLEM 332 13.5 ProbLog2 Parameter Learning 342 13.6 Parameter Learning for Hybrid Programs 343 13.7 DeepProbLog 344 13.7.1 DeepProbLog inference 346 13.7.2 Learning in DeepProbLog 347 14 Structure Learning 351 14.1 Inductive Logic Programming 351 14.2 LLPAD and ALLPAD Structure Learning 354 14.3 ProbLog Theory Compression 357 14.4 ProbFOIL and ProbFOIL+ 358 14.5 SLIPCOVER 364 14.5.1 The language bias 364 14.5.2 Description of the algorithm 364 14.5.2.1 Function INITIALBEAMS 366 14.5.2.2 Beam search with clause refinements 368 14.5.3 Execution Example 369 14.6 Learning the Structure of Hybrid Programs 372 14.7 Scaling PILP 378 14.7.1 LIFTCOVER 378 14.7.1.1 Liftable PLP 379 14.7.1.2 Parameter learning 381 14.7.1.3 Structure learning 386 14.7.2 SLEAHP 389 14.7.2.1 Hierarchical probabilistic logic programs 389 14.7.2.2 Parameter learning 397 14.7.2.3 Structure learning 409 14.8 Examples of Datasets 416 15 cplint Examples 417 15.1 cplint Commands 417 15.2 Natural Language Processing 421 15.2.1 Probabilistic context-free grammars 421 15.2.2 Probabilistic left corner grammars 422 15.2.3 Hidden Markov models 423 15.3 Drawing Binary Decision Diagrams 425 15.4 Gaussian Processes 426 15.5 Dirichlet Processes 430 15.5.1 The stick-breaking process 431 15.5.2 The Chinese restaurant process 434 15.5.3 Mixture model 436 15.6 Bayesian Estimation 437 15.7 Kalman Filter 439 15.8 Stochastic Logic Programs 442 15.9 Tile Map Generation 444 15.10 Markov Logic Networks 446 15.11 Truel 447 15.12 Coupon Collector Problem 451 15.13 One-dimensional Random Walk 454 15.14 Latent Dirichlet Allocation 455 15.15 The Indian GPA Problem 459 15.16 Bongard Problems 461 16 Conclusions 465 Bibliography 467 Index 493 About the Author 505 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 | https://ieeexplore.ieee.org/book/9999514 https://www.taylorfrancis.com/books/9781003427421 |
| work_keys_str_mv | AT riguzzifabrizio foundationsofprobabilisticlogicprogramminglanguagessemanticsinferenceandlearning |