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Trans-contextual inference system based only on distributed representations

仅基于分布式表示的跨上下文推理系统

基本信息

批准号：
15300068
负责人：
MORITA Masahiko
金额：
$ 8.38万
依托单位：
University of Tsukuba
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2006
项目状态：
已结题

项目摘要

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.

人们认为，克服经典人工智能局限性的关键是将分布式信息表示和处理为模式，而不将其符号化，并将在特定背景下学到的知识应用于各种背景下的新情况。本研究旨在开发一个具有高类比推理能力和与大脑推理规则相同的基于模式的推理系统，并取得了以下研究成果.多层神经网络是典型的基于模式的信息处理系统，但其输入输出关系对上下文的依赖性很强。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将两种分布式表示集成在一起，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。该系统通过沿着大规模动力学系统中形成的轨迹吸引子进行状态转移来推导结论，具有很强的类比推理能力。我们还证明了该系统与现有的推理系统相比具有许多优点，例如，它可以以简单的方式进行非单调推理.为了检验我们的推理系统原理的生物学可解释性，我们构建了颞下皮层和海马的模型，并将它们与神经生理学数据进行比较。这些结果，以及其他生理和心理方面的依据，强烈地表明选择性脱敏方法实际上是用于大脑的。