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，要克服经典人工智能的局限性的密钥是将分布式信息表示为模式而不象征该信息，并将在特定环境中学习的知识应用于各种情况下的新情况。本研究旨在开发一种基于模式的推理系统，该系统具有与大脑中使用的类似推理和共同原理的高能力，并获得了以下结果。1。多层神经网络是典型的基于模式的信息处理系统，难以学习很大程度上取决于上下文的输入输出关系。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，将部分神经元素脱敏。我们通过数值实验表明，该模型的学习能力和概括性能比传统的更高。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形式，这大大扩大了神经网络的潜力。2。通过将上述方法应用于称为轨迹吸引子模型的神经网络，我们仅使用分布式表示形式构建了推理系统。该系统通过在大规模动力学系统中形成的轨迹吸引子沿状态过渡来推论结论，并且具有强大的类比推理能力。我们还表明，与现有推理系统相比，该系统具有许多优势，例如，它可以以简单的方式使非单调推理。3。为了检查我们推理系统原理的生物学合理性，我们构建了颞下皮皮层和海马模型，并将其与神经生理学数据进行了比较。结果以及其他生理和心理基础，强烈建议选择性脱敏方法实际上在大脑中使用。