Working Papers of the IJCAI-2017 Workshop on Logical Foundations for Uncertainty and Machine Learning

[EN]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. This IJCAI 2017 workshop, LFU-2017, is an evolution of a series of three workshops called “Weigthed Logics for Artificial Intelligence” (WL4AI) that were successfully held in 2012 in collocation with ECAI-2012 in Montpellier (France), in 2013 in collocation with IJCAI-2013 in Beijing (China) and the third one in collocation with IJCAI-2015 in Buenos Aires (Argentina). We are very happy to gather in this proceedings volume a very interesting set of contributions on di↵erent uncertainty formalisms and approaches that we believe are representative of the richness of the area.