人类和网络颗粒推理的统一研究

Ning Zhong
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引用次数: 3

摘要

在这次演讲中,我们将从脑信息学的角度描述统一的方式研究人类和网络的颗粒推理。我们认为Web粒度推理是人类启发粒度推理的一种应用。对于人类颗粒推理,在前人关于基础水平优势及其逆转效应研究的基础上,我们利用fMRI/ERP技术研究了当起点位于基础水平时,神经系统如何相互协作和协调,以及当起点切换到更一般的水平时,大脑如何调节和适应这种变化。关键问题是“我们能否找到一种新的认知模型,用于开发基于Web的基于粒度的推理和解决问题的人类水平?”为了回答这个问题,我们研究了人类解决问题和推理的认知机制和神经基础,以开发新的认知启发的Web智能模型。基于上述结果,我们将实现问题解决器标记语言(PSML),用于表示、组织、检索和选择多级粒度的Web信息源,并开发基于PSML的Web推理引擎,用于个性化的智慧Web问题解决和服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A unified study on human and Web granular reasoning
In this talk, we will describe studying on granular reasoning of human and Web in a unified way from the viewpoint of Brain Informatics. We consider Web granular reasoning as an application of human-inspired granular reasoning. As for human granular reasoning, based on the previous studies about basic-level advantage and its reversal effect, we use fMRI/ERP to investigate how the neural system cooperates and coordinates with each other when the starting point locates in the basic level and how the brain modulates and adapts to the change when the starting point switches to a more general level. The key question is “can we find a new cognitive model for developing human-level Web based granular reasoning and problem-solving?” In order to answer this question, we investigate the cognitive mechanism and neural basis of human problem solving and reasoning, for developing new cognitively inspired Web intelligence models. Based on the above results, we will implement Problem Solver Markup Language (PSML) for representing, organizing, retrieving, and selecting Web information sources with multiple levels of granularity, and develop PSML based Web inference engines for personalized wisdom Web problem solving and services.
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