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.

中文翻译：

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学会看类比：联结主义探索

本论文通过开发名为 Analogator 的计算机程序来探索学习和类比的整合，该程序通过实例学习进行类比。通过“看到”许多不同的类比问题以及可能的解决方案，Analogator 逐渐发展出进行新类比的能力。也就是说，它学会了类比。这种方法与大多数现有的类比研究形成对比，其中通常假设模型中类比机制的先验存在。本研究通过为循环网络架构开发专门的关联训练程序，扩展了标准的连接方法。训练网络将输入场景（或情况）划分为适当的图形和地面组件。根据特定的图形和背景看一个场景，为以类似的方式看另一个场景提供了背景。经过训练，该模型能够在新情况之间进行新的类比。Analogator 与分类和识别的低级感知模型有很多共同之处；因此，它作为一个统一的框架，包括高级类比学习和低级感知。这种方法与其他类比制作的计算模型进行了比较和对比。检查了模型的训练和泛化性能，并讨论了局限性。Analogator 与分类和识别的低级感知模型有很多共同之处；因此，它作为一个统一的框架，包括高级类比学习和低级感知。这种方法与其他类比制作的计算模型进行了比较和对比。检查了模型的训练和泛化性能，并讨论了局限性。Analogator 与分类和识别的低级感知模型有很多共同之处；因此，它作为一个统一的框架，包括高级类比学习和低级感知。这种方法与其他类比制作的计算模型进行了比较和对比。检查了模型的训练和泛化性能，并讨论了局限性。