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EXPLAINABLE ARTIFICIAL INTELLIGENCE MODELING AND SIMULATION SYSTEM AND METHOD

机译:可扩译人工智能建模和仿真系统和方法

摘要

Provided is a system and method that generates an artificial intelligence workflow model in which image conversion, measurement, and image searches can be performed, by selecting/combining algorithms suitable for a workflow having been created/modified according to a subject area such as a display manufacturing process, and that conducts a simulation of the generated model. An explainable artificial intelligence modelling and simulation method according to an embodiment of the present invention comprises the steps of: designing an artificial intelligence workflow model once algorithms suitable for a workflow having been created/modified according to a subject area are selected from algorithms stored in advance; and conducting, when input information is input, a simulation of the artificial intelligence workflow model for the input information. As described above, according to embodiments of the present invention, it is possible to visualize the connections between algorithms on a workflow basis and automatically assess the performance thereof through an integrated simulation, and it is also possible to perform performance comparison by simultaneously conducting simulations of multiple artificial intelligence algorithms on a workflow basis. In addition, improvement direction for the performance of the algorithms can be suggested by allowing the result of classification analysis to explain the cause/evidence with images and standardized feature data.
机译:提供了一种系统和方法,该系统和方法通过选择/组合适用于已经创建/修改的工作流程的诸如显示器的主题区域来选择/组合算法来生成一个人工智能工作流程模型。制造过程,并进行生成模型的模拟。根据本发明的实施例的可解释的人工智能建模和仿真方法包括以下步骤:设计一个适合于根据主题区域创建/修改的工作流程的算法的人工智能工作流程模型,从预先存储的算法中选择;当输入输入信息时,进行,进行输入,用于输入信息的人工智能工作流模型的模拟。如上所述,根据本发明的实施例,可以通过集成模拟可视化工作流程的算法之间的连接,并通过集成模拟自动评估其性能,并且还可以通过同时进行模拟来执行性能比较工作流程的多个人工智能算法。另外,可以通过允许分类分析结果来解释具有图像和标准化特征数据的原因/证据来提出算法性能的改进方向。

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