PLP 2018 - Fifth Workshop on Probabilistic Logic Programming

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.
The workshop encompasses all aspects of combining logic, algorithms,
programming and probability.
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 provides a forum for the exchange of ideas, presentation of
results and preliminary work, in the following areas
* 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
* 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 above list should be interpreted broadly and is by no means exhaustive.
Purpose
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The fifth edition of PLP is held at the ILP conference in Ferrara.
We hope that this encourages further collaboration between researchers
in PLP and researchers working in other areas of ILP. In particular, we hope that both
(a) other ILP researchers will become interested in using PLP formalisms and
(b) that PLP researchers are inspired by other inductive learning approaches.
Submissions
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Submissions will be managed via EasyChair (https://easychair.org/conferences/?conf=plp2018).
Contributions should be prepared in the LNCS style.
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 6-12 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.
Publication
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Informal proceedings will be made available electronically to attendees. They
will also be stored permanently in the form of CEUR Workshop Proceedings
(http://ceur-ws.org/). The proceedings will consist of clearly marked sections
corresponding to the different types of submissions accepted.
Special Issue of IJAR
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Like for past additions of PLP, we plan to invite all authors to submit a revised version of their paper for a Probabilistic Logic Programming special issue of the IJAR journal.