Inductive techniques learn purely by observing statistical regularities in the data.
While many researchers have noted the importance of combining inductive and analytical learning, we still lack combined learning methods that are effective in practice. EBNN integrates inductive neural network learning and analytical explanation-based learning, smoothly blending both learning principles. One of the key features of EBNN is its ability to transfer knowledge from previously encountered learning tasks to other, new learning tasks. This makes it particularly applicable to scenarios in which a learner faces a whole collection of learning tasks, e.g., over its entire lifetime. In robotics domains, which will be of particular interest in this talk, the transfer of knowledge is crucial due to the costs involved with operating robot hardware.