In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
In this paper, we propose a distributed learning strategy in wireless sensor networks. Taking advantage of recent developments on kernel-based machine learning, we consider a new ...
This paper focuses on inductive learning of recursive logical theories from a set of examples. This is a complex task where the learning of one predicate definition should be inter...
Margherita Berardi, Antonio Varlaro, Donato Malerb...
We analyze why and how erroneous examples can be beneficially employed in learning mathematics. The `Why' addresses reasoning and attitudes that are rarely fostered in today&...
Weintroduce a significant improvementfor a relatively newmachine learning methodcalled Transformation-Based Learning. By applying a MonteCarlo strategy to randomly sample from the...