Description Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. eu where the code can be run online..
Many examples in the book include a link to a page of the web application http: //cplint.
The book presents the main ideas for semantics, inference, and Learning and highlights connections between the methods.
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.
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.
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.
Algorithms for the Inference and Learning tasks are then provided automatically by the system.
Probabilistic Programming extends programming Languages with Probabilistic primitives that can be used to write complex Probabilistic models.
Combining the two is a very active field of study.
Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations.
Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of Logic and probability and Probabilistic Programming.
Description Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory
