Fourth International Workshop on Statistical Relational Artificial Intelligence
STARAI@AAAI edition:4 location:Quebec date:27 July 2014
Probabilistic logic programs combine the power of a programming language with a possible world semantics, typically based on Sato's distribution semantics and they have been studied for over twenty years. In this talk, I shall report on recent progress within this paradigm. It will concern an extension towards dealing with continuous distributions as well as coping with dynamics. This is the framework of distributional clauses that has been applied to several applications in robotics, for tracking relational worlds in which objects or their properties are occluded in real time. I shall also report on an upgrade of the traditional rule learning paradigm to a probabilistic logical setting. It provides a novel perspective on learning the structure of SRL models in that it has traditional rule learners such as FOIL as a special case. The ProbFOIL system has been applied to learn rules in a probabilistic database setting and on data from CMU's Never Ending Language Learner. Finally, I shall discuss some of the open challenges within probabilistic logic programming.