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Knowledge representation method based on causal

A knowledge representation and knowledge technology, applied in the field of computer artificial intelligence to facilitate machine learning

Pending Publication Date: 2022-03-01
丹阳达创维电气设备有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But such a simple, easy-to-use, and convenient machine learning method, and a relatively basic knowledge representation method seems to have not yet appeared.

Method used

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Examples

Experimental program
Comparison scheme
Effect test

example 1

[0043] Example 1: A=K1+K2 (K1 and K2 are both information, they form information A)

[0044] A*B=C→ D, E, F, ... (basic representation)

[0045] (K1+K2)*B=C→ D, E, F, ... (substituting the obtained knowledge representation)

example 2

[0046] Example 2: B=M1+M2+M3 (M1, M2, M3 are all information, they form information B)

[0047] A*B=C→ D, E, F, ... (basic representation)

[0048] A*(M1+M2+M3)=C→D, E, F, ... (substituting the obtained knowledge representation)

example 3

[0049] Example 3: G*H=A (main part of the first knowledge)

[0050] A*B=C (the main part of the second knowledge)

[0051] (G*H) *B=C (representation formed by substituting the former body of knowledge into the latter one)

[0052] G*H*B=C (another representation formed by substituting the former knowledge subject into the latter one)

[0053] The knowledge parameters synthesized in this example need to be determined according to the actual situation, so it is temporarily ignored here.

[0054] The substitution method is not limited to the above three forms, because there are many situations, I will not list them one by one here.

[0055] Merging method is a method of putting several knowledge subjects together and using a set of parameters. Usually the knowledge put together has some kind of connection. Examples are as follows:

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PUM

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Abstract

The invention discloses a causal-based knowledge representation method, which comprises the steps that a causal knowledge structure is constructed, causal knowledge comprises knowledge subjects and knowledge parameters, and the knowledge subjects comprise data A representing preconditions, data B representing reasons and data C representing results; the parameters of the knowledge comprise at least one piece of data D representing the parameters; the following basic form is formed: A * B = C-> D; a substitution method, a merging method and a mixing method can be utilized to form subjects of other required style knowledge; providing partial data of the knowledge through the causal knowledge structure, extracting the knowledge, and calling the knowledge; necessary parameter modification can be carried out after knowledge calling so as to store use records. According to the method, knowledge to be expressed is expressed through a causal expression method, the knowledge is called according to the provided method, meanwhile, the provided machine learning method can be applied to design of an intelligent system, the method is simple and convenient to use, and machine learning is convenient to achieve.

Description

technical field [0001] The invention belongs to the technical field of computer artificial intelligence, and in particular relates to a causal-based knowledge representation method. Background technique [0002] At present, artificial intelligence is in a new upsurge of development. People once placed high expectations on the development of its technology, but soon realized that the deep learning that caused this upsurge belongs to special artificial intelligence and has inexplicability. Waiting for some defects to exist, it is difficult to meet people's expectations for the bright future of artificial intelligence in the future. Everyone is looking forward to the emergence of knowledge-driven artificial intelligence. [0003] For artificial intelligence to be knowledge-driven, it must first have a very simple and easy-to-use knowledge representation method. Although the pioneers have proposed a variety of knowledge representation methods, none of them are satisfactory, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N5/02
CPCG06N5/027G06N5/022
Inventor 葛建芳
Owner 丹阳达创维电气设备有限公司