Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning

ISBN-13: 9788770220187 / Angielski / Twarda / 2018 / 250 str.

Fabrizio Riguzzi (University of Ferrara

Opis

The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration. 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. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. 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.

Spis treści

• 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

O autorze

Fabrizio Riguzzi