Inductive programming systems characteristically exhibit an exponential explosion in search time as one increases the size of the programs to be generated. As a way of overcoming ...
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Learning systems have been devised as a way of overcoming the knowledge acquisition bottleneck in the development of knowledge-based systems. They often cast learning to a search p...
Nicola Di Mauro, Floriana Esposito, Stefano Ferill...
Machine Learning systems are often distinguished according to the kind of representation they use, which can be either propositional or first-order logic. The framework working wi...
Teresa Maria Altomare Basile, Floriana Esposito, N...
This work deals with stability in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillat...