PLP Logo PLP 2023:
The Tenth Workshop on Probabilistic Logic Programming

Collocated with ICLP 2023

London, UK, July 9-15, 2023

Important Dates  |    Accepted Papers  |    Invited Speakers  |    Programme Committee


July 9th 2023
Time (CEST)Event
13:30 - 14:10Invited Talk. Prof. Rafael Peñaloza:
14:10 - 14:35 Damiano Azzolini: A Brief Discussion about the Credal Semantics for Probabilistic Answer Set Programs
14:35 - 15:00Nicos Angelopoulos: Sampling and probabilistic inference in D/Slps
15:00 - 15:25Bao Loi Quach and Felix Weitkämper: asymptoticplp: Approximating probabilistic logic programs on large domains
15:25 - 15:50Damiano Azzolini, Elisabetta Gentili and Fabrizio Riguzzi: Link Prediction in Knowledge Graphs with Probabilistic Logic Programming: Work in Progress
16:00 - 16:30Break
16:30 - 17:10Invited Talk. Prof. Joost Vennekens:
17:10 - 17:35Kilian Rückschloß and Felix Weitkämper: On the Subtlety of Causal Reasoning in Probabilistic Logic Programming: A Bug Report about the Causal Interpretation of Annotated Disjunctions
17:35 - 18:15Invited Talk. Dr. Devendra Singh Dhami:


Probabilistic logic programming (PLP) approaches have received much attention in this century. They address the need to reason about relational domains under uncertainty arising in a variety of application domains, such as bioinformatics, the semantic web, robotics, and many more. Developments in PLP include new languages that combine logic programming with probability theory, as well as algorithms that orate over programs in these formalisms.

The workshop encompasses all aspects of combining logic, algorithms, programming and probability.

PLP is part of a wider current interest in probabilistic programming. By promoting probabilities as explicit programming constructs, inference, parameter estimation and learning algorithms can be run over programs which represent highly structured probability spaces. Due to logic programming's strong theoretical underpinnings, PLP is one of the more disciplined areas of probabilistic programming. It builds upon and benefits from the large body of existing work in logic programming, both in semantics and implementation, but also presents new challenges to the field. PLP reasoning often requires the evaluation of large number of possible states before any answers can be produced thus breaking the sequential search model of traditional logic programs.

While PLP has already contributed a number of formalisms, systems and well understood and established results in: parameter estimation, tabling, marginal probabilities and Bayesian learning, many questions remain open in this exciting, expanding field in the intersection of AI, machine learning and statistics. As is traditional in this series, the workshop would be designed to foster exchange between the various communities relevant to probabilistic logic programming, including probabilistic programming and statistical relational artificial intelligence.

This workshop provides a forum for the exchange of ideas, presentation of results and preliminary work in all areas related to probabilistic logic programming; including, but not limited to:

Important Dates

Papers due: April 30th, 2023May 08th, 2023
Notification to authors:June 08th, 2023
Camera ready version due:June 30th, 2023
Workshop date: July 09th, 2023

(the deadline for all dates is intended Anywhere on Earth (UTC-12))

Accepted Papers

Invited Speakers

Programme Committee

PC Chairs

PC Members

Last modified: 15 June 2023