T-Foresight:基于上下文感知轨迹预测来解释移动策略

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yueqiao Chen , Jiang Wu , Yingcai Wu , Dongyu Liu
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引用次数: 0

摘要

轨迹预测和解释对于优化复杂环境中的运动是至关重要的。然而,了解环境、物理和社会等不同背景因素对移动策略的影响是具有挑战性的,因为它们具有多方面的性质,这使得量化和可操作见解的推导变得复杂。我们引入了一个可解释的分析工作流程,通过创新地利用集成学习进行上下文感知轨迹预测来解决这些挑战。多个基本预测器模拟不同的移动策略,而决策模型评估每个预测器在特定环境中的适用性。这种方法通过解释决策模型的预测来量化环境因素的影响,并通过汇总基本预测器的输出来揭示可能的移动策略。T-Foresight是一个交互式可视化界面,它使利益相关者能够探索预测,解释上下文影响,并有效地设计和比较移动策略。我们在电子竞技领域评估我们的方法,特别是MOBA游戏。通过与专业分析师的案例研究,我们展示了T-Foresight在说明玩家移动策略和提供顶级战术见解方面的有效性。一项用户研究进一步证实了它在帮助普通玩家发现和理解高级策略方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
T-Foresight: Interpret moving strategies based on context-aware trajectory prediction
Trajectory prediction and interpretation are crucial in various domains for optimizing movements in complex environments. However, understanding how diverse contextual factors—environmental, physical, and social—influence moving strategies is challenging due to their multifaceted nature, which complicates quantification and the derivation of actionable insights. We introduce an interpretable analytics workflow that addresses these challenges by innovatively leveraging ensemble learning for context-aware trajectory prediction. Multiple base predictors simulate diverse moving strategies, while a decision-making model assesses the suitability of each predictor in specific contexts. This approach quantifies the impact of contextual factors by interpreting the decision-making model’s predictions and reveals possible moving strategies through the aggregation of base predictors’ outputs. The workflow comes with T-Foresight, an interactive visualization interface that empowers stakeholders to explore predictions, interpret contextual influences, and devise and compare moving strategies effectively. We evaluate our approach in the domain of eSports, specifically MOBA games. Through case studies with professional analysts, we demonstrate T-Foresight’s effectiveness in illustrating player moving strategies and providing insights into top-tier tactics. A user study further confirms its usefulness in helping average players uncover and understand advanced strategies.
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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
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