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

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 Programmin...

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Bibliographic Details
Main Author: Riguzzi, Fabrizio
Format: eBook
Language:English
Published: United Kingdom River Publishers 2018
Routledge
Edition:1
Series:River Publishers Series in Software Engineering
Subjects:
Computer programming, programs, data
Computing and Processing
INFORMATIONSCIENCEnetBASE
ITECHnetBASE
Logic programming
Probabilistic automata
Probabilities-Data processing
SCI-TECHnetBASE
Software Engineering & Systems Development
STMnetBASE
Orders
Binary Decision Diagram
Kalman Filter
BDD
Lattices
cplint Commands
Continuous Random Variables
Sld
Probability Space
Commutative Semiring
Vice Versa
Negative Literal
Composite Choice
Head Atoms
Non-Standard Inference
DS
Lifted Inference
Probabilistic Facts
Target Predicate
Monte Carlo
Probability Measure
Likelihood Weighting
Boolean Formula
Logic Programs
Exact Inference
HMM
Truel
WMC
Random Variables
Ground Atoms
PCFGs
Approximate Inference
ISBN:100079587X, 9781000795875, 9788770220187, 8770220182
Online Access:Get full text
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Description
Summary: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 of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
ISBN:100079587X
9781000795875
9788770220187
8770220182
DOI:10.1201/9781003338192