Foundations of Probabilistic Logic Programming
eBook - ePub

Foundations of Probabilistic Logic Programming

Languages, Semantics, Inference and Learning

  1. 548 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Foundations of Probabilistic Logic Programming

Languages, Semantics, Inference and Learning

About this book

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 was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration.

With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs.

Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.

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Yes, you can access Foundations of Probabilistic Logic Programming by Fabrizio Riguzzi in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half Title page
  3. Series page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Foreword
  8. Preface to the 2nd Edition
  9. Preface
  10. Acknowledgement
  11. List of Figures
  12. List of Tables
  13. List of Examples
  14. List of Definitions
  15. List of Theorems
  16. List of Acronyms
  17. 1. Preliminaries
  18. 2. Probabilistic Logic Programming Languages
  19. 3. Semantics with Function Symbols
  20. 4. Hybrid Programs
  21. 5. Semantics for Hybrid Programs with Function Symbols
  22. 6. Probabilistic Answer Set Programming
  23. 7. Complexity of Inference
  24. 8. Exact Inference
  25. 9. Lifted Inference
  26. 10. Approximate Inference
  27. 11. Non-standard Inference
  28. 12. Inference for Hybrid Programs
  29. 13. Parameter Learning
  30. 14. Structure Learning
  31. 15. Cplint Examples
  32. 16. Conclusions
  33. Bibliography
  34. Index
  35. About the Author