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An abnormity detection method for a Go AI chess manual file in an SGF format

An anomaly detection and Go technology, applied in the field of anomaly detection, can solve the problems of chess power discount, GPU and other hardware super power consumption, and heavy weight training time, and achieve the effect of improving chess power and excellent weight.

Inactive Publication Date: 2019-05-28
TONGJI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Among the many Go AIs today, the network training of weights requires more than 100,000 SGF game records, and hundreds of abnormal SGF game records are often ignored. The training data of the residual network is used to generate weights to improve the Go AI's chess performance. The weights generated by unprocessed abnormal game record training will cause Go AI to have many blind spots when playing the game, and its chess strength will be greatly reduced, and the abnormal SGF game record will consume a lot of weight training time and cause superpower consumption of GPU and other hardware

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  • An abnormity detection method for a Go AI chess manual file in an SGF format
  • An abnormity detection method for a Go AI chess manual file in an SGF format
  • An abnormity detection method for a Go AI chess manual file in an SGF format

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Embodiment Construction

[0044] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0045] The inventor found that the SGF file is a file format designed to store records of two-player chess games, and is currently the most common Go record file format, which is based on the text format.

[0046] (;GM[1]FF[4]SZ[19]PW[twoeye]WR[6d]PB[lockhart]BR[6d]CA[UTF-8]ST[2];B[pd];W[qp] ;B[dd];W[dp];B[jd];W[qf];B[nd];W[pj];B[cn];W[fq];B[dj];W[cc] ;B[cd];W[d c];B[fc];W[ec];B[ed])

[0047] The above is an example of text data in a simple SGF file. A complete SGF file must start and end with (), each separated by; is called a node. Each node can have multiple attributes, attribu...

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Abstract

The invention relates to an abnormity detection method for a Go AI chess manual file in an SGF format. The method comprises the steps that S1, importing text data of the SGF file; S2, processing the text data on the basis of a word bag model to obtain a series of word sets, and calculating the weight of each word on the basis of the obtained sets; S3, combining the weight sets of the words to obtain language model vectors of the text data; and S4, carrying out anomaly detection by utilizing an anomaly detection algorithm, and outputting a detection result. Compared with the prior art, the method has the advantages that the text is converted into the corresponding vector form based on the word bag model, so that the diagnosis can be carried out by means of some existing abnormal diagnosis algorithms, and the machine diagnosis of the chess manual file can be realized.

Description

technical field [0001] The invention relates to an abnormality detection method, in particular to an abnormality detection method for Go AI game record files in SGF format. Background technique [0002] Artificial Intelligence (AI) has been vigorously developed along with many applications in human reality scenarios, and the progress of artificial intelligence in Go has also achieved great results. SGF is the file format for saving the game record information of Go AI, and it is an important file information for Go AI weight training through the input residual network. For a long time, the number and scale of chess record files in SGF format and the complexity of chess skills have continued to grow, but Go AI cannot eliminate and identify abnormal game records in SGF format files. The abnormal game record in the SGF format file seriously affects the training data of the residual network, which leads to the expansion of the weight scale of the inferior Go weight generated by...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06F17/21
Inventor 杨恺徐悦瑶张春炯
Owner TONGJI UNIV
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