Probabilistic logic programming 2016
London, UK - 3 September 2016
NEW: Special issue
There will be a special issue on Probabilistic Logic Programming in the
International Journal of Approximate Reasoning (IJAR). We welcome submissions of
(improved/extended versions of) papers that were presented at the workshop in
London, as well as new submissions on all topics of the workshop.
For more information, please see the
for papers at the website of IJAR.
Deadline: March 1, 2017
The proceedings can be found at CEUR.
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
The workshop will take place at Room
060A of the Skempton Building of Imperial College, Prince Consort Rd, South
The fee for participating in PLP'16 is GBP40, which includes coffee
with pastry in the morning.
To register for PLP'16, please send an email to
email@example.com with your name,
affiliation, and address (which will appear on the receipt). To keep
the cost of the workshop to a minimum, we request that you do pay
the fee in cash during the workshop to one of the chairs.
Deadline for registration: 28 August 2016
- Fabrizio Riguzzi (University of Ferrara, Italy)
- Matthias Nickles (National University of Ireland, Galway)
Fabrizio Riguzzi: Deductive and Inductive Probabilistic Programming
Probabilistic programming (PP) is available in two different variants:
imperative/functional and logic. These two variants have complementary
strengths and mostly separate communities.
In this talk I will discuss how most strengths of inference for
imperative/functional PP can be included in PLP.
Moreover, I will show that PLP is particularly suitable for inductive
Matthias Nickles: Probabilistic Inductive Logic Programming Based on Answer
Answer Set Programming (ASP) is a form of declarative programming based on
the concept of so-called stable models of programs, with roots in logic
programming and nonmonotonic reasoning. ASP has emerged as a fully declarative
programming paradigm which provides significant advantages in areas such as
search and optimization problem solving, common sense knowledge representation,
and modeling of nondeterminism. In my talk, I will describe how ASP can be used
as a basis for expressive probabilistic inductive logic programming, and the
features (and challenges) of this direction. After introducing ASP and providing
an overview of existing approaches to probabilistic declarative programming
based on stable model semantics, I will present a recent framework for
probabilistic inductive ASP which provides a high level of expressiveness
(including the option to use first-order formulas with probabilities) in
combination with a high degree of adaptability to a variety of tasks. I will
discuss algorithms for inference and machine learning in this framework and
their respective performance characteristics, and present possible applications
of our framework. The last part of my talk will outline directions for future
research in this area.
|Papers due: || Fri, |10 24
June 2016 (EXTENDED)
|Notification to authors:|| Fri, 15 July 2016|
|Camera ready version due:|| Fri, 29 July 2016|
|Registration deadline: ||Sun, 28 August 2016|
|Workshop date: ||Sat, 3 September 2016|
(the deadline for all dates is 23:59 BST)
Full papers: 6-12 pages, short communications 2-5 pages.
Submissions site: easychair.
Call for papers: txt.
- Samer Abdallah (University College London)
- Arjen Hommersom
(Open University, The Netherlands)
Samer Abdallah (University College London) [co-chair]
Arjen Hommersom (Open University of the Netherlands) [co-chair]
Elena Bellodi (University of Ferrara, Italy)
Hendrik Blockeel (KU Leuven, Belgium)
Yoshitaka Kameya (Meijo University, Japan)
Wannes Meert (KU Leuven, Belgium)
Alina Paes (Universidade Federal Fluminense, Brazil)
C. R. Ramakrishnan (University at Stony Brook, US)
Taisuke Sato (NII/SONAR, Japan)
Christian Theil Have (Copenhagen University, Denmark)
Herbert Wiklicky (Imperial College London, UK)
Nicola di Mauro (University of Bari, Italy)
Nicos Angelopoulos (14M Genomics & Imperial College, UK)
Vitor Santos Costa (Universidade do Porto, Portugal)
James Cussens (University of York, UK)
Angelika Kimmig (KU Leuven, Belgium)
Evelina Lamma (University of Ferrara, Italy)
David Poole (University of British Columbia, Canada)
Luc De Raedt (KU Leuven, Belgium)
Fabrizio Riguzzi (University of Ferrara, Italy)
Alessandra Russo (Imperial College, UK)
Joost Vennekens (KU Leuven, Belgium)
Last modified: Wed 3 August 2016