Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learnin...
Stochastically searching the space of candidate clauses is an appealing way to scale up ILP to large datasets. We address an approach that uses a Bayesian network model to adaptive...
In the line of previous work by S. Muggleton and C. Sakama, we extend the logical characterization of inductive logic programming, to normal logic programs under the stable models ...
This paper presents a novel application of answer set programming to concept learning in nonmonotonic logic programs. Given an extended logic program as a background theory, we in...
Abstract. This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the...