  This dissertation explores the integration of learning and analogy-making
through the development of a computer program, called Analogator, that learns
to make analogies by example. By "seeing" many different analogy problems,
along with possible solutions, Analogator gradually develops an ability to make
new analogies. That is, it learns to make analogies by analogy. This approach
stands in contrast to most existing research on analogy-making, in which
typically the a priori existence of analogical mechanisms within a model is
assumed. The present research extends standard connectionist methodologies by
developing a specialized associative training procedure for a recurrent network
architecture. The network is trained to divide input scenes (or situations)
into appropriate figure and ground components. Seeing one scene in terms of a
particular figure and ground provides the context for seeing another in an
analogous fashion. After training, the model is able to make new analogies
between novel situations. Analogator has much in common with lower-level
perceptual models of categorization and recognition; it thus serves as a
unifying framework encompassing both high-level analogical learning and
low-level perception. This approach is compared and contrasted with other
computational models of analogy-making. The model's training and generalization
performance is examined, and limitations are discussed.
