Probabilistic Logic Programming (PLP) introduces probabilistic reasoning in Logic Programs in order to represent uncertain information. It is receiving an increased attention due to its applications in particular in the Machine Learning field. In this tutorial we will show how to use cplint on SWISH, a web application for performing inference and learning on user-defined probabilistic logic programs. You will learn how to write a probabilistic logic program processable by cplint on SWISH, how to execute the different types of queries allowed by this application and how to perform learning. cplint on SWISH is based on SWISH, a web framework for Logic Programming, and on cplint , a suite of programs for inference and learning of Logic Programs with annotated disjunctions (LPADs) . It keeps the same syntax of cplint and as cplint it uses Logic Programs with annotated disjunctions (LPADs) as formalism to represent probabilistic logic programs.

Probabilistic Logic Programming Tutorial

RIGUZZI, Fabrizio;COTA, Giuseppe
2016

Abstract

Probabilistic Logic Programming (PLP) introduces probabilistic reasoning in Logic Programs in order to represent uncertain information. It is receiving an increased attention due to its applications in particular in the Machine Learning field. In this tutorial we will show how to use cplint on SWISH, a web application for performing inference and learning on user-defined probabilistic logic programs. You will learn how to write a probabilistic logic program processable by cplint on SWISH, how to execute the different types of queries allowed by this application and how to perform learning. cplint on SWISH is based on SWISH, a web framework for Logic Programming, and on cplint , a suite of programs for inference and learning of Logic Programs with annotated disjunctions (LPADs) . It keeps the same syntax of cplint and as cplint it uses Logic Programs with annotated disjunctions (LPADs) as formalism to represent probabilistic logic programs.
Probabilistic Logic Programming, Distribution Semantics, Probabilistic Inductive Logic Programming, Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2350948
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