Trans-contextual inference system based only on distributed representations

克服经典人工智能局限性的关键是将分布式信息以模式的形式表示和处理，而不是将其符号化，并将在特定环境中学习到的知识应用于各种环境中的新情况。本研究旨在开发一种基于模式的推理系统，该系统具有较高的类比推理能力，并且与大脑中使用的推理原理相同，并获得了以下结果。多层神经网络是典型的基于模式的信息处理系统，在学习输入输出关系方面存在一定的困难。为了克服这一困难，我们开发了选择性脱敏方法，根据上下文对部分神经元素进行脱敏。数值实验表明，该模型具有比传统模型更高的学习能力和泛化性能。这是因为在不使用局部表示的情况下集成了两种分布式表示，极大地扩展了神经网络的潜力。通过将上述方法应用于一种称为轨迹吸引子模型的神经网络，我们构建了一个仅使用分布式表示的推理系统。该系统通过在大尺度动力系统中形成的轨迹吸引子的状态跃迁推导出结论，具有强大的类比推理能力。我们还证明了该系统比现有的推理系统有许多优点，例如，它可以以简单的方式进行非单调推理。为了检验推理系统原理的生物学合理性，我们构建了颞下皮层和海马体模型，并将其与神经生理学数据进行了比较。这些结果，连同其他生理学和心理学的依据，有力地表明，选择性脱敏方法实际上在大脑中使用。

It is considered that keys to overcome the limitations of classical artificial intelligence are to represent and process distributed information as patterns without symbolizing it, and to apply knowledge learned in a particular context to new situations in various contexts. The present study aimed to develop a pattern-based reasoning system that has high ability of analogical reasoning and common principles to that used in the brain, and obtained the following results.1. Multilayer neural networks, which are typical pattern-based information processing systems, have difficulty in learning input-output relation that depends strongly on context. To overcome this difficulty, we developed the selective desensitization method, with which part of neural elements are desensitized depending on the context. We show by numerical experiments that this model has much higher learning capacity and generalization performance than the conventional ones. This is because two kinds of distributed representations are integrated without using local representations, which greatly expands the potential of neural networks.2. By applying the above method to a kind of neural network called a trajectory attractor model, we built a reasoning system using distributed representations alone. This system deduces a conclusion by state transitions along a trajectory attractor formed in a large-scale dynamical system, and has powerful ability of analogical reasoning. We also showed that this system has many advantages over existing reasoning systems, for example, it can make nonmonotonic reasoning in a simple manner.3. To examine biological plausibility of the principles of our reasoning system, we constructed models of inferotemporal cortex and hippocampus and compare them with neurophysiological data. The results, together with other physiological and psychological grounds, strongly suggested that the selective desensitization method is actually used in the brain.