Simulation of interactive action sequences using the example of high-performance soccer.

在提高行为数据的电子可收集性和人工智能支持的可分析性的过程中，对分析的动态过程可以优化的期望越来越高。在复杂的互动交流过程中，这种技术控制更有问题，因为这里除了功能层面之外还涉及认知和情感层面。人工智能支持的认知过程分析的问题在于，虽然可以对可观察到的行为进行分类和评估，但由于行为动力学的复杂性，沟通和实施适当或最佳的行为模式通常是困难的。这导致了这样一个事实，即复杂的行动建议被代理简化为高度简化的模式，当不加批判地实现时，这随后会导致刻板的行为。体育游戏是一种社会高度相关的过程形式，它包含了认知、交流和互动动态的复杂组合。除了一些合作游戏的方法，大多数游戏都是以胜利为导向的，即游戏目标是取得比对手更好的结果。体育游戏是基于计算机的数据收集和行为优化的热门“游乐场”，尤其属于这一类。一个非常突出的例子就是足球，最近在欧洲杯上，它为这种基于计算机和人工智能的分析和优化的可能性和局限性提供了说明材料。同时，获取和解释球员和球的位置数据、移动过程和动作模式以及战术-战略成功评估的技术也非常发达。将其转化为游戏动作的能力还有待完善。将潜在的无限复杂的分析结果转移到实践中，从而转移到教练-球员和球员-球员之间的交流中，导致基本建议的减少，从而由于缺乏预期而导致刻板印象的增加。这方面的一个例子是“TikiTaka”控球策略。过去几年，在比赛数据采集和比赛分析技术得到改进的同时，这种减少的战略概念也显著增加。特别是今年的欧洲杯，也展示了战略链之间长期的刻板的战术互动。当前应用的目的是，基于已完成项目的积极结果，在计算机辅助游戏分析的结果与更生动的游戏设计的可能性之间建立更好的联系，在这种情况下，成功不是通过战术“推动”，而是通过创造性地分解战术计划来实现的。为此目的，前一个项目开始的创造性培训办法将继续下去，并在更高的执行水平上具体执行、检验和评价。

In the course of increasing electronic collectability and AI-supported analyzability of behavioral data, the expectation that the analyzed dynamic processes can be optimized is growing. In complex interactive-communicative processes, such technical controls are more problematic, since here cognitive and emotional levels are addressed in addition to functional ones. The problem with AI-supported analysis in cognitive processes is that while observable behavior can be categorized and evaluated, communicating and implementing adequate or optimal behavioral patterns is often difficult due to the complexity of the behavioral dynamics. This leads to the fact that complex recommendations for action are reduced by the agents to highly simplified patterns, which subsequently lead to stereotypical behavior when implemented uncritically. A socially highly relevant process form that contains a complex combination of cognitive, communicative and interactive dynamics is that of the (sports) game. Apart from some approaches to cooperative games, the majority of games are win-oriented, i.e., the game goal is to achieve a better result compared to the opponents. Sports games, which are a popular "playground" for computer-based data collection and behavioral optimization, especially belong to this category. A very prominent example of this is soccer, which most recently provided illustrative material for the possibilities and limitations of such computer- and AI-based analysis and optimization in the European Championship. The acquisition and interpretation technology for e.g. position data of players and ball, movement processes and action patterns up to the tactical-strategic evaluation of success is meanwhile very well developed. Less well developed is the ability to translate this into game action.The transfer of the potentially infinitely complex analysis results into practice and thus to the coach-player and player-player communications leads to a reduction to the basic recommendations and thus to an increasing stereotypy due to a lack of anticipation. An example of this is the "TikiTaka" ball possession strategy. The last few years have shown, parallel to the technical improvement of match data acquisition and match analysis, a significant increase of such reduced strategic concepts. And this year's European Championship in particular also showed stereotypical tactical interactions between the strategic chains over long stretches.The aim of the present application is, building on the positive results of the completed project, to establish a better link between the results of computer-aided game analysis and the possibilities of a more lively game design, in which success is achieved not through tactical "pushing" but through creative break-ups of tactical schemes. To this end, the approaches to creative training begun in the previous project are to be continued and specifically implemented, tested and evaluated at higher performance levels.