[DL] IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Learning

Marcelo Finger mfinger at ime.usp.br
Tue May 2 17:05:46 CEST 2017

IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Learning

The purpose of this workshop is to promote logical foundations for
reasoning and learning under uncertainty. Uncertainty is inherent in
many AI applications, and coping with this uncertainty, in terms of
preferences, probabilities and weights, is essential for the system to
operate purposefully. In the same vein, expecting a domain modeler to
completely characterize a system is often unrealistic, and so enabling
mechanisms by means of which the system can infer and learn about the
environment is needed. While probabilistic reasoning and Bayesian
learning has enjoyed many successes and is central to our current
understanding of the data revolution, a deeper investigation on the
underlying semantical issues as well as principled ways of extending
the frameworks to richer settings is what this workshop strives for.
Broadly speaking, we aim to bring together the many communities
focused on uncertainty reasoning and learning -- including knowledge
representation, machine learning, logic programming and databases --
by focusing on the logical underpinnings of the approaches and

Given the intent of the workshop, we encourage two categories of submissions:

On the practical side, we solicit papers that propose ways to bridge
conventional learning and inference techniques with deductive and
inductive reasoning. Driven by the successes of relational graphical
models and statistical relational learning, we especially encourage
papers that emphasize or demonstrate non-standard logical features in
systems, e.g. the ability to handle infinite domains, existential
uncertainty and/or function symbols.
On the foundations side, we solicit papers that explicate the use of
weights in reasoning and learning, e.g. the use of weight functions
such as degrees of belief, preferences, and truth degrees. We
especially encourage papers that demonstrate how non-standard weight
functions for reasoning and learning can be better integrated with
existing probabilistic methods. The idea, then, is to foster
collaboration between machine learning practitioners and the weighted
logic community. For example, we encourage papers that revisit the
learning objectives and inference methodologies of existing systems,
and propose novel semantical frameworks to understand them.

In essence, this workshop builds on and extends the scope of the
successful series WL4AI (Weighted Logics for Artificial Intelligence:
ECAI-2012, IJCAI-2013, IJCAI-2015) in additionally looking at the
semantical  foundations of machine learning and exploring practical

Topics include (but are not limited to):

Probabilistic and weighted databases and knowledge bases
Integration of deductive and inductive reasoning with Bayesian
inference and learning
Semantical foundations for machine learning
Logics for data-intensive information processing, such as data fusion
Extension of statistical relational learning with generic weight functions
Declarative methods for inference and learning

Paper Format and Submission

We invite technical papers (up to 6 pages), position papers (up to 2 pages).

We invite submissions describing either work in progress or mature
work that has already been published at other research venues and
would be of interest to researchers working on the themes above.
Submission of previously published work in whole or in part may be in
the form of a resubmission of a previous paper, or in the form of a
position paper that overviews and cites a body of work.

All papers should be typeset in the IJCAI style, described at

Papers should be submitted via EasyChair at

Please see the website for up to date information (including
submission procedure):


Important Dates

Paper Submission: May 15
Author Notification: June 8
Camera ready: July 15
Workshop Date: August 19

Organizing Committee

Vaishak Belle, University of Edinburgh, vaishak(at)ed.ac.uk
Marcelo Finger, University of Sao Paulo, mfinger(at)ime.usp.br
James Cussens, University of York, james.cussens(at)york.ac.uk
Guilin Qi, Southeast University, China, gqi(at)seu.edu.cn
Henri Prade, Universite Paul Sabatier, France, prade(at)irit.fr
Lluis Godo, IIIA CSIC, Spain, godo(at)iiia.csic.es

Program Committee

Fabio Cozman, University of Sao Paulo, Brazil
Jesse Davies, KU Leuven, Belgium
Adnan Darwiche, UCLA, USA
Didier Dubois, IRIT, France
Esra Erdem, Sabanci University, Turkey
Linda van der Gaag, Universiteit Utrecht, The Netherlands
Tommaso Flaminio, University of Insubria, Italy
Vibhav Gogate, University of Texas at Dallas, USA
Joe Halpern, Cornell University, USA
Manfred Jaeger, Aalborg University, Denmark
Souhila Kaci, University Montpellier, France
Gabriele Kern-Isberner, Technical University of Dortmund, Germany
Gerhard Lakemeyer, RWTH Aachen University, Germany
Churn-Jung Liau, Academia Sinica, Taiwan
Emiliano Lorini, IRIT, France
Thomas Lukasiewicz, University of Oxford, UK
Carsten Lutz, University of Bremen, Germany
Denis Maua, University of Sao Paulo, Brazil
Vanina Martinez, Universidad Nacional del Sur, Argentina
Zoran Ognjanovic, Mathematical Institute SANU, Serbia
Ron Petrick, Edinburgh, UK
Rodrigo De Salvo Braz, SRI, USA
Giuseppe Sanfilippo, Univ. Catania, Italy
Steven Schockaert, Cardiff University, UK
Guillermo Simari, Universidad Nacional del Sur, Argentina
Umberto Straccia, CNR, Italy

 Marcelo Finger
 Departament of Computer Science, IME
 University of Sao Paulo

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