DECISION SUPPORT SYSTEM FOR DETERMINING THE OPTIMAL COMPOSITION OF THE TEAM OF PERFORMERS ON THE EXAMPLE OF THE SPORTS VERSION OF THE GAME “WHAT? WHERE? WHEN?"
The paper describes the task to create a decision support system that allows you to determine the optimal composition of the team of performers. The sports version of the game “What? Where? When?" is chosen. The principles of holding tournaments on the intellectual game “What? Where? When?" and the rules for the formation of teams to participate in such competitions. It is concluded that to predict the impact of changes in the team composition on the result, it is advisable to use modern mathematical and intellectual methods, including the method of artificial neural networks. The available data on the results of synchronous tournaments LUK (ST) since 2011 and city tournaments (GT) since 2017 are presented, the main indicators for each competition are characterized. The introduction of the output factors is substantiated: the ratio of the team’s result to the average result and the ratio of the team’s result to the winner’s result. The forecasting problem is formulated as a prediction of the relative result of a team on a specific game based on the available list of team players for this game. It is proposed to take into account the location of a particular game, and fix the participation of players in the form of a “share” of the contribution to the team’s result, while the sum of the “shares” of all players should be equal to one. A method of artificial neural networks with a two-layer perceptron architecture, a sigmoid activation function and an error propagation algorithm for training a network is proposed. Examples of calculation in the Deductor Studio Lite environment are given. It is concluded that for the practical application of the model, the constant use of standard packages is inapplicable. In addition, it is also necessary to solve the problem of automating the selection of the team composition. Described is an application developed in a visual programming environment – a decision support system that allows you to import source data from an XLS file, configure input and output factors, change the architecture of the neural network (the number of hidden layers and the number of neurons in each layer), train the neural network using the backpropagation of errors, save the trained network on disk and load it again, calculate values for the input data, search for options for the composition of the team. The developed decision support system makes it possible to give recommendations on the formation of a team for a specific tournament by enumerating options.
Keywords: intellectual games, questions package, team building, forecasting, artificial neural network, perceptron, sigmoid, network training.
Galkina T.P. Sotsiologiya upravleniya: ot gruppy k komande: Ucheb. posobie [Sociology of management: from group to team: Textbook. allowance]. Moscow, Finance and Statistics Publ., 2003. 224 p.
Gallert Manfred. Vse o komandoobrazovanii: rukovodstvo dlya trenerov [All About Team Building: A Guide for Coaches] / Translated from German. Manfred Gellert, Klaus Novak. Moscow: Vershina Publ, 2006. 352 p.
Semina A. P. Komanda kak gruppovaya forma organizatsii truda. [Team as a group form of work organization.]. Bulletin of the Altai Academy of Economics and Law. 2019. № 12-1. С. 128–133. Available at: https://vaael.ru/ru/article/view?id=858 (accessed 18.03.2021)
Liga ukrainskikh klubov – LUK [League of Ukrainian Clubs – LUC]. Available at: http://luk.org.ua/ (accessed 16.03.2021).
Reglament provedeniya Chempionata Ukrainy po sportivnomu variantu intellektual’noy igry «Chto? Gde? Kogda?» bez ogranicheniya vozrasta [Regulations of the Championship of Ukraine in the sports version of the intellectual game “What? Where? When?" no age limit]. URL: https://goo.gl/DhJDHc (accessed 16.03.2021).
Klub intellektual’nykh igr DGMA [Club of intellectual games of DSEA]. Available at: https://www.facebook.com/groups/dgma.kii/ (accessed 16.03.2021).
Polozhenie o sostavakh komand-uchastnits Chempionata Ukrainy po sportivnomu variantu igry «Chto? Gde? Kogda?» bez ogranicheniya vozrasta [Regulations on the composition of the teams participating in the Championship of Ukraine in the sports version of the game “What? Where? When?" no age limit]. Available at: http://luk.org.ua/documents/polozhenie-o-sostavah (accessed 16.03.2021).
Kasyuk S. T., Vakhtomova E. M. Ispol’zovanie neyronnykh setey dlya analiza i prognozirovaniya dannykh v fizicheskoy kul’ture i sporte [Using neural networks for data analysis and forecasting in physical culture and sports]. Scientific and theoretical journal "Scientific notes", 2013, № 12 (106), pp. 72–77.
Krutikov A. K. Prognozirovanie sportivnykh rezul’tatov v individual’nykh vidakh sporta s pomoshch’yu obobshchennoregressionnoy neyronnoy seti [Prediction of sports results in individual sports using a generalized regression neural network]. Young scientist, 2018, № 12, pp. 22–26. Available at: https://moluch.ru/archive/198/48884/ (accessed 20.01.2020).
Kallan R. Osnovnye kontseptsii neyronnykh setey [Basic concepts of neural networks]. Moscow: Williams Publ., 2001. 288 с.
Khaykin S. Neyronnye seti: polnyy kurs, 2-e izdanie [Neural Networks: Complete Course, 2nd Edition] / Translation from English. Moscow: Williams Publ., 2006. 1104 с.
Kovalevskiy S. V., Gitis V. B. Sozdanie i primenenie neyronnykh setey dlya resheniya prikladnykh zadach: Uchebno-metodicheskoe posobie dlya studentov spetsial’nosti «Intellektual’nye sistemy prinyatiya resheniy» [Creation and application of neural networks for solving applied problems: Study guide for students of the specialty "Intelligent decision-making systems"]. Kramatorsk: DSEA Publ., 2008. 75 p.
BaseGroup Labs: ofitsial’nyy sayt [BaseGroup Labs: official site]. Available at: https://basegroup.ru/community/articles/intro (accessed 16.03.2021).
Mel’nikov A. Yu. O vozmozhnostyakh primeneniya neyrosetevogo modelirovaniya dlya opredeleniya optimal’nogo sostava komandy po igre «Chto? Gde? Kogda?» i prognozirovaniya ee rezul’tatov [On the possibilities of using neural network modeling to determine the optimal team composition for the game “What? Where? When?" and predicting its results]. Neural network technologies and their application NMTiZ-2018: a collection of scientific papers of the AllUkrainian scientific conference with international participation "Neural network technologies and their application NMTiZ-2018" / for general. ed. SV Kovalevsky. Kramatorsk: DSEA Publ., 2018, pp. 71–74.
Mel’nikov A. Yu. Primenenie neyrosetevogo modelirovaniya dlya opredeleniya optimal’nogo sostava komandy v igre «Chto? Gde? Kogda?» i prognozirovaniya ee rezul’tatov [Application of neural network modeling to determine the optimal composition of the team in the game “What? Where? When?" and predicting its results]. Bulletin of the Donbass State Engineering Academy: a collection of scientific papers. Kramatorsk: DSEA Publ., 2020, № 1 (48), pp. 154– 160.
«Chto? Gde? Kogda?»: chetvertyy gorodskoy turnir ["What? Where? When? ": Fourth city tournament]. Available at: https://www.facebook.com/groups/dgma.kii/permalink/1539402616 220690/ (accessed 16.03.2021).
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