A significant challenge in teaching programming to disadvantaged populations is preserving learners’ motivation and confidence. Because programming requires such a diverse set o...
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily ...
In this paper, we present an experimental methodology and results for a machine learning approach to learning opening strategy in the game of Go, a game for which the best compute...
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...