In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, combining the probability estimates from the target task with those from the auxiliary tasks. To validate our approach, we used three Bayesian networks models that are commonly used for evaluating learning techniques, and generated variants of each model by changing the structure as well as the parameters. We then learned one of the variants with a small dataset and combined it with information from the other variants. The experimental results show a significant improvement in terms of structure and parameters when we transfer knowledge from similar tasks. We also evaluated the method with real-world data from a manufacturing process considering several products, obtaining an improvement in terms of log-likelihood between the data and the model when we do transfer learning from related products.

This chapter provides a concise introduction to Reinforcement Learning (RL) from a machine learning perspective. It provides the required background to understand the chapters related to RL in this book. It makes no assumption on previous knowledge in this research area and includes short descriptions of some of the latest trends, which are normally excluded from other introductions or overviews on RL. The chapter provides more emphasis on the general conceptual framework and ideas of RL rather than on presenting a rigorous mathematical discussion that may require a great deal of effort by the reader. The first section provides a general introduction to the area. The following section describes the most common solution techniques. In the third section, some of the most recent techniques proposed to deal with large search spaces are described. Finally, the last section provides some final remarks and current research challenges in RL.