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

使用高性能足球示例模拟交互式动作序列。

• 批准号: 405976247
• 负责人: Professor Dr. Daniel Memmert
• 金额: --
• 依托单位: Institut für Trainingswissenschaft und Sportinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/405976247?language=en
• 关键词: Simulation; interactive; action; sequences; using

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.

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进行优化的期望正在增长。在复杂的互动交流过程中，这种技术控制更成问题，因为这里除了功能性控制外，还涉及认知和情感层面。认知过程中AI支持的分析的问题在于，虽然可以对可观察到的行为进行分类和评估，但由于行为动态的复杂性，沟通和实施适当或最佳的行为模式往往很困难。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，随后在不加批判地实施时导致刻板行为。一个社会高度相关的过程形式，包含了一个复杂的组合的认知，沟通和互动的动态是（体育）游戏。除了一些合作博弈的方法外，大多数博弈都是以赢为导向的，即，游戏的目标是取得比对手更好的成绩。体育游戏是基于计算机的数据收集和行为优化的流行“游乐场”，尤其属于这一类。一个非常突出的例子是足球，最近在欧洲锦标赛中提供了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的说明性材料。同时，球员和球的位置数据、运动过程和动作模式的获取和解释技术，直到成功的战术-战略评估，都得到了很好的发展。将潜在的无限复杂的分析结果转化为实践，从而转化为教练与球员以及球员与球员之间的交流，导致基本建议的减少，从而导致由于缺乏预期而增加的刻板印象。这方面的一个例子是“TikiTaka”控球策略。过去几年已经表明，与比赛数据采集和比赛分析的技术改进平行，这种简化的战略概念显著增加。特别是今年的欧洲锦标赛也显示了战略链之间长时间的常规战术互动。本申请的目的是，在已完成项目的积极结果的基础上，在计算机辅助游戏分析的结果和更生动的游戏设计的可能性之间建立更好的联系，成功不是通过战术上的“推动”，而是通过创造性地打破战术计划。为此，将继续采用上一个项目中开始的创造性培训方法，并在更高的绩效水平上具体实施、测试和评估。