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

along with any other PLP-related topic.