lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. explanation-based neural network learning (ebnn) is a machine learning algorithm that transfers knowledge across multiple learning tasks. when faced with a new learning task, ebnn exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. as a result, ebnn generalizes more accurately from less data than comparable methods. explanation-based neural network learning: a lifelong learning approach describes the basic ebnn paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `the paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm. from the foreword by tom m. mitchell.