Real-world biological multi-agent behaviors such as team sports are often largely unknown due to their inherently higher-order interactions,cognition, and body dynamics . Estimation of the rules from data provides an effective way for the analysis of such behaviors . Survey focuses on data-driven analysis for quantitativeunderstanding of team sports behaviors . It introduces two main approaches for understanding such multi-agents behaviors . The first approach involves the visualization of learned representations and theextraction of mathematical structures behind the behaviors . Second approach is used to test hypotheses by simulating and controlling future andcounterfactual behaviors . These approaches can contribute to a better understanding of multi-Agent behaviors inthe real world. These approaches are discussed. The potential practical applications of theseapproaches can . contribute to . a betterĀ approaches are discussed . Theseapproaches could contribute to an understanding of mult-agent

Author(s) : Keisuke Fujii

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Keywords : behaviors - understanding - sports - analysis - contribute -

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