PLP 2024: the Eleventh Workshop on Probabilistic Logic Programming

Hybrid Event - Colocated with ICLP 2024

Dallas, Texas - October 13, 2024

About the Workshop

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

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.

PLP is part of a wider current interest in probabilistic programming. 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.

While PLP has already contributed a number of formalisms, systems and well understood and established results in, such as, 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.

Scope

Topics of interest include, but are not limited to:

This list is by no means exhaustive.

Programme

Time Speaker(s) Title
11:00 - 11:45 Thomas Eiter Invited Talk: Visual Question Answering Using ASP
11:45 - 12:00 Michela Vespa, Elena Bellodi, Federico Chesani, Daniela Loreti, Paola Mello, Evelina Lamma, Anna Ciampolini Probabilistic Compliance in Declarative Process Mining
12:00 - 12:15 Michela Vespa, Elena Bellodi, Federico Chesani, Daniela Loreti, Paola Mello, Evelina Lamma, Anna Ciampolini, Marco Gavanelli, Riccardo Zese Probabilistic Traces in Declarative Process Mining
12:15 - 12:30 Damiano Azzolini, Fabrizio Riguzzi Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization
12:30 - 13:45 Lunch Lunch
13:45 - 14:30 Theresa Swift Invited Talk: Probabilistic Logic Programming, Belief Functions and Computer Vision
14:30 - 14:45 Damiano Azzolini, Matteo Bonato, Elisabetta Gentili, Fabrizio Riguzzi Logic Programming for Knowledge Graph Completion
14:45 - 15:00 Mario Alviano, Antonio Ielo, Francesco Ricca Efficient Compliance Computation in Probabilistic Declarative Specifications
15:00 - 15:15 J.-Martín Castro-Manzano Statistical Syllogistic Tableaux
15:15 - 15:30 Damiano Azzolini, Markus Hecher A First Journey into the Complexity of Statistical Statements in Probabilistic Answer Set Programming
15:30 - 16:00 Break Break
16:00 - 16:15 Fabrizio Riguzzi Quantum Algorithms for Weighted Constrained Sampling and Weighted Model Counting
16:15 - 17:00 Mario Alviano Invited Talk: Generative Datalog and Monte Carlo Model Enumeration: An ASP-Based Approach
17:00 Closing Closing

Call for Papers

We invite three types of submissions: At least one author of each accepted paper will be required to attend the workshop (either in presence or remotely) to present the contribution.

Remote participation will be allowed.

Papers should follow the standard CEUR template available at: https://www.overleaf.com/read/zdmkmssdrdtq#93a571

Submission will be managed via Easychair: https://easychair.org/conferences/?conf=plp2024

Important Dates

Proceedings

Original papers will be published as CEUR workshop proceedings. We are discussing the possibility of inviting some selected papers to a special issue on an international journal.

Invited Speakers

Thomas Eiter (TU Wien)

Title: Visual Question Answering Using ASP
Abstract: Answer Set Programming (ASP), is a well-known approach to declarative problem solving that has been successfully employed in many application areas. Among them is Visual Question Answering (VQA), which is concerned with answering a question, posed in natural language, about a visual scene shown in an image or possibly also in a video sequence. VQA is a challenging task that requires processing multi-modal input and reasoning capabilities to obtain the correct answer. In this talk, we consider VQA using ASP in a modular neuro-symbolic architecture that comprises both subsymbolic components, based on neural networks, and symbolic reasoning components that use ASP. We present instantiations of the architecture and discuss usages such as explanation finding, which benefit from the versatility of ASP and the rich landscape of ASP extensions. Furthermore, we report on ongoing work at the knowledge-based systems group of TU Wien, which is concerned with exploiting LLMs.

Theresa Swift (Stony Brooke University)

Title: Probabilistic Logic Programming, Belief Functions and Computer Vision
Abstract: TBA

Mario Alviano (University of Calabria)

Title: Generative Datalog and Monte Carlo Model Enumeration: An ASP-Based Approach
Abstract: This talk explores the recent advancements in Generative Datalog, focusing on its implementation in Answer Set Programming (ASP) for enumerating models using a Monte Carlo approach. Generative Datalog, which enhances Datalog with constructs for parameterized probability distributions, enables the specification of complex stochastic processes. The latest implementation introduces the ability to define probability mass functions, allowing for smarter enumeration strategies that avoid duplicate models

Programme Committee

PC Chairs

PC Members

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