职业足球策略学习与推理的马尔可夫框架

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, L. D. Raedt, Jesse Davis
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引用次数: 0

摘要

策略优化是动态和复杂团队运动(如足球、美式足球和篮球)的基本元素。随着从这些运动的比赛中收集的数据量的增加,对数据驱动的决策支持的需求也在增加。如果需要平衡备选策略,那么数据驱动的方法可以揭示定性分析无法获得的见解。这可以极大地帮助球队准备比赛。在这项工作中,我们提出了一个新的基于马尔可夫模型的足球框架,该框架允许对球队使用的特定策略进行推理,以便深入了解每种策略的效率。该框架由两个部分组成:(1)学习部分,它需要通过从团队比赛中收集的事件数据中学习马尔可夫决策过程(MDP)来建模团队的进攻行为;(2)推理部分,它涉及到概率模型检查的新应用,以推理每个团队学习策略的有效性。在本文中,我们提供了该框架的概述,并使用来自三个联盟的真实事件数据在几个用例中说明了它。我们的研究结果表明,该框架可以用来推理球队的射门决策,并优化与特定球队比赛时使用的防守策略。在这个框架中提出的一般思想可以很容易地扩展到其他运动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer
Strategy-optimization is a fundamental element of dynamic and complex team sports such as soccer, American football, and basketball. As the amount of data that is collected from matches in these sports has increased, so has the demand for data-driven decisionmaking support. If alternative strategies need to be balanced, a data-driven approach can uncover insights that are not available from qualitative analysis. This could tremendously aid teams in their match preparations. In this work, we propose a novel Markov modelbased framework for soccer that allows reasoning about the specific strategies teams use in order to gain insights into the efficiency of each strategy. The framework consists of two components: (1) a learning component, which entails modeling a team’s offensive behavior by learning a Markov decision process (MDP) from event data that is collected from the team’s matches, and (2) a reasoning component, which involves a novel application of probabilistic model checking to reason about the efficacy of the learned strategies of each team. In this paper, we provide an overview of this framework and illustrate it on several use cases using real-world event data from three leagues. Our results show that the framework can be used to reason about the shot decision-making of teams and to optimise the defensive strategies used when playing against a particular team. The general ideas presented in this framework can easily be extended to other sports.
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来源期刊
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research 工程技术-计算机:人工智能
CiteScore
9.60
自引率
4.00%
发文量
98
审稿时长
4 months
期刊介绍: JAIR(ISSN 1076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes. Established in 1993 as one of the first electronic scientific journals, JAIR is indexed by INSPEC, Science Citation Index, and MathSciNet. JAIR reviews papers within approximately three months of submission and publishes accepted articles on the internet immediately upon receiving the final versions. JAIR articles are published for free distribution on the internet by the AI Access Foundation, and for purchase in bound volumes by AAAI Press.
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