In the real world, information could have levels of uncertainty, so it becomes essential to provide methods for representing such type of information. To cover this need, the research group on Artificial Intelligence of the University of Ferrara, of which Arnaud Nguembang Fadja is a member, proposed two reasoning systems based on the language of distributional semantics a few years ago. The two systems, called EMBLEM and SLIPCOVER, are very effective in terms of the result provided but are very expensive in terms of learning and reasoning time. Arnaud Nguembang Fadja then proposed a new knowledge representation language called "Liftable Probabilistic Logic Programs (LPLP)
" in which reasoning and learning are much faster. He has implemented several systems of reasoning and learning both parameters and the structure of programs based on the LPLP language. He developed the algorithm, LIFTCOVER for LIFTed slipCOVER
, which learns the structure of LPLP from the data. Two versions of LIFTCOVER have been proposed: the first, LIFTCOVER-EM, uses the Expectation Maximization (EM) algorithm as a subroutine for learning the parameters, and the second, LIFTCOVER-LBFGS, uses an optimization method called BFGS with limited memory. Publications
- Lifted discriminative learning of probabilistic logic programs
, published in a Machine learning journal.
- Lifted discriminative learning of probabilistic logic programs, published at the 27th International Conference on Inductive Logic Programming, ILP 2017, 2017