EFFECTIVE MANAGEMENT OF THE INTERACTION OF SPORTS TEAM MEMBERS BY USING ARTIFICIAL INTELLIGENCE IDEAS

IF 0.2 Q4 SPORT SCIENCES
M. Koliada, T. Bugayova, E. Reviakina, S. Belykh, G. Kapranov
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引用次数: 1

Abstract

Aim. The objective of the article is to explain both clearly and scientifically the theoretical and methodological foundations of decision-making based on the ideas of artificial intelligence. Materials and methods. We justified the necessity of taking into account the psychological factors connected with coach’s willingness to position players correctly and to achieve the best possible result in the conditions of the game’s unpredictability. The scientific application of the mechanisms for searching the effective interaction of sports team members was given with the help of a genetic algorithm. Results. We revealed the relevance of the issue of players positioning in terms of their better interaction for coaches and sports managers. Practical recommendations were given for a better understanding of decision-making based on the so-called ‘reserved algorithm’. The performance of Darwin’s algorithm in searching for optimal players positioning was demonstrated in details. The efficiency of such an algorithm was proved by making possible to find the best solution in a few steps. An example of the most popular software product for solving such problems in computer intelligent environments is given. Conclusion. We made a conclusion that by using intelligent systems it is possible to perform accurate and objective calculations in the management of sports team members. This also allows making both operational and final decisions regarding the interaction of own and opponent’s team members, which makes possible achieve high results. A coach or PE teacher can forecast precisely achievements in team sports. The application of genetic algorithm is a calculated guarantee of high achievements and the condition for improving quantitative methods in pedagogy.
利用人工智能思想有效管理运动队成员之间的互动
的目标。本文的目的是清晰而科学地解释基于人工智能思想的决策的理论和方法基础。材料和方法。我们证明了考虑心理因素的必要性,这些因素与教练的意愿有关,教练愿意正确地安排球员,并在比赛不可预测的情况下取得最好的结果。利用遗传算法,科学地应用了搜索运动团队成员有效互动的机制。结果。我们揭示了球员在教练和体育经理之间更好的互动方面的定位问题的相关性。为了更好地理解基于所谓的“保留算法”的决策,给出了实用的建议。详细论证了达尔文算法在寻找最优玩家位置方面的性能。通过在几个步骤中就可以找到最优解,证明了该算法的有效性。给出了在计算机智能环境中解决这类问题的最流行的软件产品的一个例子。结论。我们得出的结论是,通过使用智能系统,可以在运动队成员管理中进行准确客观的计算。这也让我们能够针对自己和对手的团队成员之间的互动做出操作性和最终性的决定,从而有可能获得更高的结果。教练或体育老师可以准确地预测团队运动的成绩。遗传算法的应用是教学量化方法取得优异成绩的计算保证和改进教学量化方法的条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Human Sport Medicine
Human Sport Medicine SPORT SCIENCES-
CiteScore
0.70
自引率
50.00%
发文量
0
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