Abstract
The goal of this project is to develop inference and learning techniques for proba- bilistic logic programs, with an eye on the scalable automatic induction of proba- bilistic rules from large datasets. Such techniques have applications in information search and retrieval, automated diagnosis, decision and recommendation systems - applications that benefit from large and accurate knowledg…