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River Publishers Series in Software EngineeringSeries

Foundations of Probabilistic Logic Programming

Languages, Semantics, Inference and Learning

Hardback
October 2018
9788770220187
More details
  • Publisher
    River Publishers
  • Published
    29th October
  • ISBN 9788770220187
  • Language English
  • Pages 250 pp.
$110.00
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October 2018
9788770220170
More details
  • Publisher
    River Publishers
  • Published
    22nd October
  • ISBN 9788770220170
  • Language English
  • Pages 250 pp.
$82.50

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 in the book include a link to a page of the web application http://cplint.eu where the code can be run online.

Preface
1. Preliminaries
2. Probabilistic Logic Programming Languages
3. Semantics with Function Symbols
4. Semantics for Hybrid Programs
5. Exact Inference
6. Lifted Inference
7. Approximate Inference
8. Non-Standard Inference
9. Parameter Learning
10. Structure Learning
11. cplint Examples
12. Conclusions
Index

Fabrizio Riguzzi

Fabrizio Riguzzi is at University of Ferrara, Italy.