Game creativity analysis using neural networks
Game creativity analysis using neural networks
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DOI：
10.1080/02640410802442007
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发表时间：
2009-01-01
影响因子：
3.4
通讯作者：
Perl, Juergen
中科院分区：
文献类型：
--
作者：
Memmert, Daniel;Perl, Juergen
球类运动专家的特点是具有非凡的创造性行为。本文提出了一个基于神经网络的创造性绩效个体发展类型分析框架。因此，两种运动的具体训练方案的游戏创造力的学习在真实的领域的背景下进行了调查。两个训练组（足球，n=20;曲棍球，n=17）而非对照组（n=18）在三个测量点方面有所改善（P 0.001），尽管两个训练组之间没有差异（P=0.212）。通过使用神经网络，现在可以区分五种类型的学习行为，其中最引人注目的是我们所说的上下和上下。特别是在曲棍球组，一个上下波动的过程中被确定，从而创造性的性能最初增加，但在年底是比在中间的培训计划。相反的上下波动过程主要是在足球组。结果进行了讨论，最近的训练解释模型，如超补偿理论，以期进一步发展神经网络的应用。
Experts in ball games are characterized by extraordinary creative behaviour. This article outlines a framework for analysing types of individual development of creative performance based on neural networks. Therefore, two kinds of sport-specific training programme for the learning of game creativity in real field contexts were investigated. Two training groups (soccer, n=20; field hockey, n=17) but not a control group (n=18) improved with respect to three measuring points (P0.001), although no difference could be established between the two training groups (P=0.212). By using neural networks it is now possible to distinguish between five types of learning behaviour in the development of performance, the most striking ones being what we call up-down and down-up. In the field hockey group in particular, an up-down fluctuation process was identified, whereby creative performance increases initially, but at the end is worse than in the middle of the training programme. The reverse down-up fluctuation process was identified mainly in the soccer group. The results are discussed with regard to recent training explanation models, such as the super-compensation theory, with a view to further development of neural network applications.