Introduction to the special issue on probability, logic and learning. Issue 2 (March 2015)

• Record Type: Journal Article
• Authors: CUSSENS, JAMES; DE RAEDT, LUC; KIMMIG, ANGELIKA; SATO, TAISUKE
• Is Part Of: Theory and practice of logic programming. Volume 15:Issue 2(2015)
• Page Start: 145
• Page End: 146
• Publication Date: 2015-03
• Subjects: Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068413000665
• Languages: English
• ISSNs: 1471-0684

Abstract

<abstract abstract-type="text-abstract"> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <p>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 <italic>et al</italic>. (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.</p> </abstract>