19 Aug 2017
Melbourne, Australia
IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Learning http://homepages.inf.ed.ac.uk/vbelle/workshops/lfu17/ Overview 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 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 issues. 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 http://ijcai-17.org/FormattingGuidelinesIJCAI-17.zip Papers should be submitted via EasyChair at https://easychair.org/conferences/?conf=lfu17 Please see the website for up to date information (including submission procedure): http://homepages.inf.ed.ac.uk/vbelle/workshops/lfu17/ 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 -- [LOGIC] mailing list http://www.dvmlg.de/mailingliste.html Archive: http://www.illc.uva.nl/LogicList/ provided by a collaboration of the DVMLG, the Maths Departments in Bonn and Hamburg, and the ILLC at the Universiteit van Amsterdam