Negnevitsky人工智能英文讲义课件.ppt

How to make a machine learn, or the rebirth of neural networks (mid-1980s – onwards) In the mid-eighties, researchers, engineers and experts found that building an expert system required much more than just buying a reasoning system or expert system shell and putting enough rules in it. Disillusions about the applicability of expert system technology even led to people predicting an AI “winter” with severely squeezed funding for AI projects. AI researchers decided to have a new look at neural networks. By the late sixties, most of the basic ideas and concepts necessary for neural computing had already been formulated. However, only in the mid-eighties did the solution emerge. The major reason for the delay was technological: there were no PCs or powerful workstations to model and experiment with artificial neural networks. In the eighties, because of the need for brain-like information processing, as well as the advances in computer technology and progress in neuroscience, the field of neural networks experienced a dramatic resurgence. Major contributions to both theory and design were made on several fronts. Grossberg established a new principle of self-organisation (adaptive resonance theory), which provided the basis for a new class of neural networks (Grossberg, 1980). Hopfield introduced neural networks with feedback – Hopfield networks, which attracted much attention in the eighties (Hopfield, 1982). Kohonen published a paper on self-organising maps (Kohonen, 1982). Barto, Sutton and Anderson published their work on reinforcement learning and its application in control (Barto et al., 1983). But the real breakthrough came in 1986 when the back-propagation learning algorithm, first introduced by Bryson and Ho in 1969 (Bryson Ho, 1969), was reinvented by Rumelhart and McClelland in Parallel Distributed Processing (1986). Artificial neural networks have come a long way from the early models of McCulloch and Pitts to an interdisciplinary subject with roots