Probabilistic Logic Programming in Action

Abstract

Probabilistic Programming (PP) has recently emerged as an effective approach for building complex probabilistic models. Until recently PP was mostly focused on functional programming while now Probabilistic Logic Programming (PLP) forms a significant subfield. In this paper we aim at presenting a quick overview of the features of current languages and systems for PLP. We first present the basic semantics for probabilistic logic programs and then consider extensions for dealing with infinite structures and continuous random variables. To show the modeling features of PLP in action, we present several examples: a simple generator of random 2D tile maps, an encoding of Markov Logic Networks, the truel game, the coupon collector problem, the one-dimensional random walk, latent Dirichlet allocation and the Indian GPA problem. These examples show the maturity of PLP.

Syntactic Requirements for Well-defined Hybrid Probabilistic Logic Programs

“…The power and expressivity of Probabilistic Logic Programming (PLP) [8,18] have been utilized to represent many real world situations [2,9,14]. Usually, probabilistic logic programs involve only discrete random variables with Bernoulli or Categorical distributions.…”

A Comparison of MCMC Sampling for Probabilistic Logic Programming

“…Probabilistic Logic Programming (PLP) is a useful paradigm for encoding models characterized by complex relations heavily depending on probability [10,13]. One of the main challenges of PLP is to find the probability distribution of query random variables, a task called inference.…”

“…This program, differently from the others, is hybrid, i.e., it contains also continuous random variables. For this test, we fix both the number of words considered in a document (10) and the number of topics (2) and we compute the relation between number of samples, probability and execution time. In this example, we observe that the first two words of the document are equal, which has probability 0.01.…”

Lifted discriminative learning of probabilistic logic programs

“…Probabilistic Logic Programming (PLP) is a powerful tool for reasoning in uncertain relational domains that is gaining popularity in Statistical Relational Artificial Intelligence (StarAI) due to its expressiveness and intuitiveness. PLP has been applied successfully to a variety of fields, such as natural language processing (Sato and Kubota 2015;Riguzzi et al 2017b;Nguembang Fadja and Riguzzi 2017), bioinformatics (Mørk and Holmes 2012;De Raedt et al 2007;Sato and Kameya 1997), link prediction in social networks (Meert et al 2010), entity resolution (Riguzzi 2014) and model checking (Gorlin et al 2012).…”