用多模态模型预测多人游戏中玩家未来位置，提升智能体导航与策略推荐能力。
A Multimodal Architecture for Endpoint Position Prediction in Team-based Multiplayer Games
- 基于U-Net生成位置概率热图，融合图像、数值、类别及动态数据
- 通过多头注意力机制实现团队间信息交互，提升预测精度
- 适用于玩家行为分析、异常检测和预测性智能体开发
理解并预测多人游戏中的玩家移动对实现玩家模仿机器人导航、预判式机器人控制、策略推荐和实时玩家行为分析等应用至关重要。然而，复杂环境带来高度自由的移动空间，且玩家间的互动与团队协作要求模型能有效利用异构输入数据。本文提出一种多模态架构，用于在动态时间范围内预测玩家未来位置，采用基于U-Net的方法计算终点位置概率热图，并通过多模态特征编码器进行条件化。利用多头注意力机制对不同特征组进行通信，实现代理间的信息交互。该架构高效整合了包括图像输入、数值与类别特征以及动态游戏数据在内的多源异构信息。所提方法为依赖未来玩家位置的下游任务（如构建预测型玩家行为机器人或玩家异常检测）奠定了基础。
原文摘要 · Abstract (English)
Understanding and predicting player movement in multiplayer games is crucial for achieving use cases such as player-mimicking bot navigation, preemptive bot control, strategy recommendation, and real-time player behavior analytics. However, the complex environments allow for a high degree of navigational freedom, and the interactions and team-play between players require models that make effective use of the available heterogeneous input data. This paper presents a multimodal architecture for predicting future player locations on a dynamic time horizon, using a U-Net-based approach for calculating endpoint location probability heatmaps, conditioned using a multimodal feature encoder. The application of a multi-head attention mechanism for different groups of features allows for communication between agents. In doing so, the architecture makes efficient use of the multimodal game state including image inputs, numerical and categorical features, as well as dynamic game data. Consequently, the presented technique lays the foundation for various downstream tasks that rely on future player positions such as the creation of player-predictive bot behavior or player anomaly detection.
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