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.
Topics of interest include, but are not limited to:
This list is by no means exhaustive.
Submission will be managed via Easychair: https://easychair.org/conferences/?conf=plp2024