基于游戏的自动学习信息支持的认知建模

Y. A. Khalin, A. I. Katykhin, S. Zinkin, A. Shilin
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引用次数: 0

摘要

研究目的。这项工作的主要目标是提高基于认知建模的自动游戏学习的效率。基于系统分析和弱结构情境认知建模的方法,提出了游戏自动化训练综合体的结构,该综合体可用于多个学科领域的人才培训。在我们的案例中,员工培训在刻板印象和非刻板印象的情况下进行了模拟。这项工作是基于系统理论和系统分析的一般规定,数学图论(这是基于认知建模)。认知建模的主要工具是Silov模糊认知图的构建。对模糊认知地图主要系统指标的计算算法进行了改进。基于商业游戏的游戏建模被用于自动学习。引入了作战博弈的概念,利用模糊认知图对某些不利情况的发展进行了建模。本工作的主要成果是基于游戏的自动学习信息支持的认知建模方法。在此基础上,对作战游戏《房屋火灾》进行了基于模糊认知图构建的博弈仿真仿真,计算了相互影响、协调和不协调的主要定量系统指标。结论:开发的方法允许对不利情况(包括紧急情况)进行游戏建模,这将在未来确保学生在真实情况下的适当行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Modeling of Information Support for Game-Based Automated Learning
Purpose of research. The main goal of this work is to increase the efficiency of automated game learning based on cognitive modeling. Based on the methodology of system analysis and cognitive modeling of weakly structured situations, the structure of a gaming automated training complex is proposed, which can be used in training personnel in various subject areas. In our case, staff training was simulated in stereotypical and non-stereotypical situations.Methods. This work is based on the general provisions of systems theory and system analysis, mathematical graph theory (which is based on cognitive modeling). The main tool of cognitive modeling was the construction of fuzzy cognitive maps of Silov. A modification of the algorithm for calculating the main system indicators of a fuzzy cognitive map was proposed. Game modeling, based on business games, was used for automated learning. The concept of an operational game was introduced, then modeling of the development of some unfavorable situation was carried out using fuzzy cognitive maps..Results. he main result of this work is the method of cognitive modeling of information support for game-based automated learning. Based on the developed methodology, a game simulation simulation of the operational game "House Fire" was carried out, which was based on the construction of a fuzzy cognitive map, for which the main quantitative system indicators of mutual influence, consonance and dissonance were calculated.Conclusion: the developed methodology allows for game modeling of unfavorable (including emergency) situations, which in the future will ensure adequate behavior of students in real situations.
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