Naive Bayesian classifiers have been very successful in attribute-value representations. However, it is not clear how the decomposition of the probability distributions on attribu...
Induction of recursive theories in the normal ILP setting is a complex task because of the non-monotonicity of the consistency property. In this paper we propose computational solu...
Floriana Esposito, Donato Malerba, Francesca A. Li...
A framework for combining Genetic Algorithms with ILP methods is introduced and a novel binary representation and relevant genetic operators are discussed. It is shown that the pro...
Hierarchical reinforcement learning has been proposed as a solution to the problem of scaling up reinforcement learning. The RLTOPs Hierarchical Reinforcement Learning System is an...
A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduc...