基于任意概念的学习和推理认知模型

Kieran R. C. Greer
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引用次数: 5

摘要

本文描述了一种新的认知模型,可以用来构建一个系统,它可以智能地实现概念,然后对它们进行推理。主要的模型以前已经发表过,但本文提出了一个新的更精细的处理级别,它将允许系统自己学习任意复杂的概念。然后可以将它们聚集成代表更高级别概念的链,并进行推理。这些链条还可以相互触发,产生一定程度的“思考”。该模型既适用于类神经系统,也适用于大型分布式网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Cognitive Model for Learning and Reasoning over Arbitrary Concepts
This paper describes a new cognitive model that could be used to build a system that can intelligently realise concepts for itself and then reason over them. The main model has been published previously, but this paper proposes a new finer level of processing that would allow the system to learn arbitrarily complex concepts for itself. These can then be clustered into chains that represent higher level concepts and reasoned over. These chains can also trigger each other to generate a certain level of ‘thinking'. This model would be suitable for a neural-like system, but also a large distributed network.
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