There has recently been considerable attention concerning the combination of probability, logic and learning in the artificial intelligence and machine learning communities. 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. On the other hand, much of the contemporary work in this area is very much application and experiment driven, and often devotes less attention to the general culture of logic programming which is concerned with the theoretical foundations of the underlying formalisms and inference procedures and with advanced implementation technology that scales well.
For this special issue we invite high-quality papers on all aspects of probability, logic and learning. Of particular interest are papers that contribute theoretical insights, discuss advanced implementation techniques, or propose probabilistic programming pearls. Probabilistic programming pearls are examples of elegant and effective probabilistic logic programs. High quality review papers are also welcome.
Specific topics of interest include:
- Lifted inference techniques for probabilistic logic programming
- Studies of probabilistic and logical inference methods
- Deciding and acting using probabilistic logics
- Dynamic probabilistic logics
- Learning in probabilistic logics
- Inductive logic programming
- Relationships of probabilistic logic programming to other areas
- * Probabilistic databases
Papers can be submitted by email to email@example.com.
Submission deadline: April 2, 2012
Notification of authors: July 15, 2012
James Cussens (University of York)
Luc De Raedt (Katholieke Universiteit Leuven)
Angelika Kimmig (Katholieke Universiteit Leuven)
Taisuke Sato (Tokyo Institute of Technology)