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

by Fabrizio Riguzzi

Overview

This book aims at providing an overview of probabilistic logic programming with a special emphasis on languages under the distribution semantics, and presents the main ideas for semantics, inference, and learning and highlights connections between the methods.

Product Details

ISBN-13:	9781000923223
Publisher:	River Publishers
Publication date:	07/07/2023
Sold by:	Barnes & Noble
Format:	eBook
Pages:	548
File size:	9 MB

About the Author

Fabrizio Riguzzi is Full Professor of Computer Science at the Department of Mathematics and Computer Science of the University of Ferrara. He was previously Associate Professor and Assistant Professor at the same university. He obtained his Masters and PhD in Computer Engineering from the University of Bologna. Fabrizio Riguzzi is Editor in Chief of Intelligenza Artificiale, the official journal of the Italian Association for Artificial Intelligence. He is the author of more than 200 peer reviewed papers in the areas of machine learning, inductive logic programming and statistical relational learning. His aim is to develop intelligent systems by combining in novel ways techniques from artificial intelligence, logic and statistics.

Table of Contents

Foundations of Probabilistic Logic Programming

Editorial Reviews

"Probabilistic logic programming (PLP) is an attempt to find a bridge between computational logic and modern machine learning methods. This book gives a nice, self-contained introduction to PLP. It consists of 12 chapters, which can be grouped into five parts: preliminaries, languages, semantics, inference, and learning...The book will be useful for readers with a background in computer science and artificial intelligence who want to learn about principles, techniques, and systems for probabilistic logic programming."

MathSciNet