The 9th Workshop on Probabilistic Logic Programming

Collocated with ICLP 2022

August 1st, 2022, Haifa, Israel

Important Dates | Submission | Programme Committee

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 operate over programs in these formalisms.

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 that represent highly structured probability spaces. Partly due to logic programming's strong theoretical underpinnings, PLP is fast becoming a very well founded area 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 a 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. The workshop encompasses all aspects of combining logic, algorithms, programming and probability. It aims to bring together researchers in all aspects of probabilistic logic programming, including theoretical work, system implementations and applications. Interactions between theoretical and applied minded researchers are encouraged.

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:

- probabilistic logic programming formalisms
- probabilistic logic programming languages
- parameter estimation
- statistical inference
- implementations
- structure learning
- reasoning with uncertainty
- constraint store approaches
- stochastic and randomised algorithms
- probabilistic knowledge representation and reasoning
- neuro-symbolic representation and reasoning
- constraints in statistical inference
- PLP applications, such as bioinformatics, semantic web, robotics,...
- probabilistic graphical models
- Bayesian learning
- tabling for learning and stochastic inference
- MCMC
- stochastic search
- labelled logic programs
- integration of statistical software

The registration for PLP2022 is managed through the ICLP registration system. To register, visit the ICLP registration page.

Papers due: | |

Notification to authors: | July 10th, 2022 |

Camera ready version due: | July 20th, 2022 |

Workshop date: | August 1st, 2021 |

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

- Alexander Artikis & Periklis Mantenogloy (University of Piraeus, Greece)
- Vaishak Belle (University of Edinburgh, Scotland)

**Online Reasoning under Uncertainty with the Event Calculus**

Activity recognition systems detect temporal combinations of 'low-level' or 'short-term' activities on sensor data streams. Such streams exhibit various types of uncertainty, often leading to erroneous recognition. We will present an extension of an interval-based activity recognition system which operates on top of a probabilistic Event Calculus implementation. Our proposed system performs online recognition, as opposed to batch processing, thus supporting streaming applications. Our empirical analysis demonstrates the efficacy of our system, comparing it to interval-based batch recognition, point-based recognition, as well as structure and weight learning models.

**Explainability, causality and computational and-or graphs**

In recent years, there has been an increasing interest in studying causality-related properties in machine learning models generally, and in generative models in particular. While that is well-motivated, it inherits the fundamental computational hardness of probabilistic inference, making exact reasoning intractable. Probabilistic tractable models have also recently emerged, which guarantee that conditional marginals can be computed in time linear in the size of the model, where the model is usually learned from data. In the talk, we will discuss a number of new results in this area. We will discuss what kind of casual queries can be answered on trained tractable models, what kind of domain constraints can be posed and what methods are available to extract (counterfactual) explanations from them.

Submissions will be managed via EasyChair: https://easychair.org/conferences/?conf=plp22.

Contributions should be prepared in the 1-column CEURART style (also available as an overleaf project). A mixture of papers are sought including: new results, work in progress as well as technical summaries of recent substantial contributions. Papers presenting new results should be 6-15 pages in length. Work in progress and technical summaries can be shorter (2-5 pages). The workshop proceedings will clearly indicate the type of each paper.

At least one author of each accepted paper will be required to attend the workshop to present the contribution.

- Nicos Angelopoulos (Wellcome Sanger Institute, UK)
- Elena Bellodi (University of Ferrara, Italy)
- Fabio Cozman (University of São Paulo, Brazil)
- Luke Dickens (University College London, UK)
- Markus Hecher (Vienna University of Technology, Austria)
- Arjen Hommersom (Open University of the Netherlands, The Netherlands)
- Evelina Lamma (University of Ferrara, Italy)
- Matthias Nickles (National University of Ireland Galway, Ireland)
- Fabrizio Riguzzi (University of Ferrara, Italy)
- Rolf Schwitter (Macquarie University, Australia)
- Joost Vennekens (KU Leuven, Belgium)

Last modified: 10 June 2022