Foundations of probabilistic logic programming : languages, semantics, inference and learning / Fabrizio Riguzzi.

Format:

Book

Author/Creator:

Riguzzi, Fabrizio, author.

Series:

River Publishers series in software engineering.

River Publishers Series in Software Engineering

Language:

English

Subjects (All):

Probabilities--Data processing.

Probabilities.

Logic programming.

Physical Description:

1 online resource (xxxiii, 387 pages).

Place of Publication:

Gistrup, Denmark : River Publishers, [2018]

Summary:

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 model 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 the 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. Foundations of Probabilistic Logic Programming 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.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Foreword

Preface

Acknowledgments

List of Figures

List of Tables

List of Examples

List of Definitions

List of Theorems

List of Abbreviations

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

2: Probabilistic Logic Programming Languages

2.1 Languages with the Distribution Semantics

2.1.1 Logic Programs with Annotated Disjunctions

2.1.2 ProbLog

2.1.3 Probabilistic Horn Abduction

2.1.4 PRISM

2.2 The Distribution Semantics for Programs Without Function Symbols

2.3 Examples of Programs

2.4 Equivalence of Expressive Power

2.5 Translation to Bayesian Networks

2.6 Generality of the Distribution Semantics

2.7 Extensions of the Distribution Semantics

2.8 CP-Logic

2.9 Semantics for Non-Sound Programs

2.10 KBMC Probabilistic Logic Programming Languages

2.10.1 Bayesian Logic Programs

2.10.2 CLP(BN)

2.10.3 The Prolog Factor Language

2.11 Other Semantics for Probabilistic Logic Programming

2.11.1 Stochastic Logic Programs

2.11.2 ProPPR

2.12 Other Semantics for Probabilistic Logics

2.12.1 Nilsson's Probabilistic Logic

2.12.2 Markov Logic Networks

2.12.2.1 Encoding Markov Logic Networks with Probabilistic Logic Programming

2.12.3 Annotated Probabilistic Logic Programs

3: Semantics with Function Symbols

3.1 The Distribution Semantics for Programs with Function Symbols

3.2 Infinite Covering Set of Explanations

3.3 Comparison with Sato and Kameya's Definition

4: Semantics for Hybrid Programs.

4.1 Hybrid ProbLog

4.2 Distributional Clauses

4.3 Extended PRISM

4.4 cplint Hybrid Programs

4.5 Probabilistic Constraint Logic Programming

4.5.1 Dealing with Imprecise Probability Distributions

5: Exact Inference

5.1 PRISM

5.2 Knowledge Compilation

5.3 ProbLog1

5.4 cplint

5.5 SLGAD

5.6 PITA

5.7 ProbLog2

5.8 TP Compilation

5.9 Modeling Assumptions in PITA

5.9.1 PITA(OPT)

5.9.2 MPE with PITA

5.10 Inference for Queries with an Infinite Number of Explanations

5.11 Inference for Hybrid Programs

6: Lifted Inference

6.1 Preliminaries on Lifted Inference

6.1.1 Variable Elimination

6.1.2 GC-FOVE

6.2 LP2

6.2.1 Translating ProbLog into PFL

6.3 Lifted Inference with Aggregation Parfactors

6.4 Weighted First-Order Model Counting

6.5 Cyclic Logic Programs

6.6 Comparison of the Approaches

7: Approximate Inference

7.1 ProbLog1

7.1.1 Iterative Deepening

7.1.2 k-best

7.1.3 Monte Carlo

7.2 MCINTYRE

7.3 Approximate Inference for Queries with an Infinite Number of Explanations

7.4 Conditional Approximate Inference

7.5 Approximate Inference by Sampling for Hybrid Programs

7.6 Approximate Inference with Bounded Error for Hybrid Programs

7.7 k-Optimal

7.8 Explanation-Based Approximate Weighted Model Counting

7.9 Approximate Inference with TP-compilation

7.10 DISTR and EXP Tasks

8: Non-Standard Inference

8.1 Possibilistic Logic Programming

8.2 Decision-Theoretic ProbLog

8.3 Algebraic ProbLog

9: Parameter Learning

9.1 PRISM Parameter Learning

9.2 LLPAD and ALLPAD Parameter Learning

9.3 LeProbLog

9.4 EMBLEM

9.5 ProbLog2 Parameter Learning

9.6 Parameter Learning for Hybrid Programs

10: Structure Learning

10.1 Inductive Logic Programming

10.2 LLPAD and ALLPAD Structure Learning.

10.3 ProbLog Theory Compression

10.4 ProbFOIL and ProbFOIL+

10.5 SLIPCOVER

10.5.1 The Language Bias

10.5.2 Description of the Algorithm

10.5.2.1 Function INITIALBEAMS

10.5.2.2 Beam Search with Clause Refinements

10.5.3 Execution Example

10.6 Examples of Datasets

11: cplint Examples

11.1 cplint Commands

11.2 Natural Language Processing

11.2.1 Probabilistic Context-Free Grammars

11.2.2 Probabilistic Left Corner Grammars

11.2.3 Hidden Markov Models

11.3 Drawing Binary Decision Diagrams

11.4 Gaussian Processes

11.5 Dirichlet Processes

11.5.1 The Stick-Breaking Process

11.5.2 The Chinese Restaurant Process

11.5.3 Mixture Model

11.6 Bayesian Estimation

11.7 Kalman Filter

11.8 Stochastic Logic Programs

11.9 Tile Map Generation

11.10 Markov Logic Networks

11.11 Truel

11.12 Coupon Collector Problem

11.13 One-Dimensional Random Walk

11.14 Latent Dirichlet Allocation

11.15 The Indian GPA Problem

11.16 Bongard Problems

12: Conclusions

References

Index

About the Author.

Notes:

Description based on print version record.

ISBN:

1-00-333819-4

1-003-33819-4

1-000-79255-2

87-7022-017-4

9781003338192

OCLC:

1059547836