Introduction to the special issue on probability, logic and learning. Issue 2 (March 2015) - CUSSENS, JAMES; DE RAEDT, LUC; KIMMIG, ANGELIKA; SATO, TAISUKE
<x content-type="archive" xml:space="preserve">Abstract</x> Recently, the combination of probability, logic and learning has received considerable attention in the artificial intelligence and machine learning communities; see e.g. Getoor and Taskar (2007); De Raedt et al. (2008). Computational logic often plays a major role in these developments since it forms the theoretical backbone for much of the work in probabilistic programming and logical and relational learning. Contemporary work in this area is often application- and experiment-driven, but is also concerned with the theoretical foundations of formalisms and inference procedures and with advanced implementation technology that scales well.