3rd Workshop on Probabilistic Logic Programming
Imperial College, London, UK, 03/09/2016
hosted by ILP 2016
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 ran 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. This workshop 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. The presence of this workshop at ILP is intended to encourage collaboration with researchers from the field of Inductive Logic Programming. 
Topics of Interest 
 probabilistic logic programming formalisms
 parameter estimation
 statistical inference
 implementations
 structure learning
 reasoning with uncertainty
 constraint store approaches
 stochastic and randomised algorithms
 probabilistic knowledge representation and reasoning
 constraints in statistical inference
 applications, such as
 bioinformatics
 music
 robotics
 semantic web
 probabilistic graphical models
 Bayesian learning
 tabling for learning and stochastic inference
 MCMC
 stochastic search
 labelled logic programs
 integration of statistical software
