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.
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