|Invited speaker:||James Cussens, University of York, UK|
|Workshop takes place on:||17th of July|
|Special issue:||International Journal of Approximate Reasoning [cfp] [PLP @ IJAR]|
The workshop's programme is now avaiable on-line
Deadline for early registration is 8th of June, register through VSL: site
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 braking 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. PLP will run concurrently with the first day of CICLOPS, the premium workshop of LP implementations. We hope to generate interest in probabilistic LP among the CICLOPS participants that can lead to collaborative research.
Submissions site: easychair.
Call for papers: txt, pdf.