Description

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 techniques.

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 sum, 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

Dates (Tentative)

  • Paper Submission: May 15 May 28 (extended deadline)
  • Author Notification: Jun 8
  • Camera ready: Jul 15
  • Workshop Date: Aug 19

Committees

Organizing Committee

Vaishak Belle, University of Edinburgh, UK; vaishak(at)ed.ac.uk

Marcelo Finger, University of Sao Paulo, Brazil; mfinger(at)ime.usp.br

James Cussens, University of York, UK; 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, University of Palermo, Italy

Steven Schockaert, Cardiff University, UK

Guillermo Simari, Universidad Nacional del Sur, Argentina

Umberto Straccia, CNR, Italy

Schedule

TBA

Submission

We invite technical papers (up to 6 pages), and extended abstracts (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 in the areas of reasoning about uncertainty and learning. 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 an extended abstract that overviews and cites a body of work.

Submissions of papers being reviewed at other venues are welcome since this is a non archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission. Otherwise, no anonymization is required.

All papers should be typeset in the IJCAI-17 style, described at http://ijcai-17.org/FormattingGuidelinesIJCAI-17.zip

Papers should be submitted via EasyChair at https://easychair.org/conferences/?conf=lfu17

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