This research seeks to combine the two primary paradigms for machine learning: inductive and analytical learning. Inductive methods such as instance-based and neural network learning can reliably learn simple functions from noisy data, but require vast numbers of training examples in order to scale up to very complex functions. In contrast, analytical methods such as explanation-based learning can learn complex functions from much less data, but rely upon strong prior knowledge on the part of the learner. Much current research in machine learning seeks to combine the best of both approaches, to obtain methods that learn more correct generalizations from approximate prior knowledge together with observed data. The proposed research takes a novel approach to this problem: unifying neural network learning and explanation- based learning. More specifically, this research will build on the recently developed explanation-based neural network (EBNN) learning method. Preliminary research has demonstrated experimentally that EBNN can generalize better from fewer examples than pure inductive learning if accurate domain knowledge is available, and that it degrades gracefully with the quality of the learner's prior knowledge. This research will explore more fully the space of combined neural net and explanation-based methods, focusing on issues such as scaling up to more complex learning tasks, alternative types of information that can be extracted from explanations based on neural networks, operating robustly over the entire spectrum from very strong to very weak prior knowledge, and alternative representations for the domain theory and target function. EBNN learning will be applied to two different task domains. If successful, this research could produce learning methods that scale up to more practical problems, and lead to a clearer understanding of the correspondence between symbolic and neural network approaches.
