Abstract: In scenarios related to human-agent collaboration, the explicit/legible representation and accurate recognition of agent intent serve as vital means to enhance collaborative safety and efficiency, and constitute the foundation for establishing and improving human trust in agents. The core lies in enhancing the legibility of agent behaviors to facilitate effective human understanding. Existing research primarily conveys intent by introducing differentiated behaviors that deviate from the optimal path. While effective in certain contexts, such schemes inherently sacrifice partial execution efficiency. In highly confusing scenarios, they face two critical challenges: 1) In environments characterized by overlapping and dense routes, sacrificing efficiency yields diminishing returns in intent conveyance; 2) Overlapping routes obscure the informational value of the agents' original behavioral sequences, making it difficult for observers to discern their true intentions. Addressing these issues, this study investigates key factors influencing behavioral legibility in complex human collaboration, focusing on action posture, velocity, and distance. Without altering the agents' original action sequences, this paper proposes a multi-dimensional fusion representation mechanism through the multi-dimensional modeling and integration of key state information, including posture, velocity, and distance. Specifically, we design a posture mapping function aligned with human reasoning, utilize Gaussian kernel functions to transform and map velocity and distance representations, and integrate these features via fusion parameters to enhance behavioral legibility. Furthermore, corresponding to this mechanism, we propose an efficient intent recognition scheme based on Bayesian update. By fully leveraging the fused state information, the scheme achieves rapid and accurate identification of agent intent. Consequently, the proposed method—which integrates the mechanism and the scheme—not only enhances the explicit expressive capability of overall behaviors regarding their intent but also improves task execution efficiency by reducing the number of differentiated behaviors required. The method is comprehensively validated across three experimental scenarios: unconstrained, complex obstacle-laden, and highly confusing environments. Experimental results demonstrate that, compared to baseline methods, our approach incorporates additional dimensions of state information, improving the accuracy of agent intent recognition by an average of 44.47%. Moreover, by conveying sufficient intent information through shorter behavioral sequences, the method significantly boosts task execution efficiency, reducing agent execution costs by an average of 42.86%. Ablation studies verify the positive contributions of posture, velocity, and distance to legibility enhancement, with posture identified as the most significant factor. Sensitivity analysis determines the optimal value ranges for color and velocity factor parameters, ensuring stable performance. Additionally, experiments on continuous control tasks confirm the method's applicability in complex engines and real-world engineering. On the algorithmic side, complexity analysis reveals that the inference time increases by only 2.89 milliseconds compared to the base method, satisfying real-time interaction requirements. This study provides a novel pathway for reducing redundant behaviors while enhancing legibility, balancing belief transmission and execution efficiency without disrupting original action sequences. The method can be directly embedded into various baseline approaches to achieve varying degrees of improvement in recognition accuracy. Future wor

摘要： 在人机协同相关场景下，智能体意图的显式/可读表示与准确识别是提升协作安全以及效率的重要手段，也是建立及提升人类对智能体信任度的重要依托。其核心在于增强智能体行为的可读性，以便人类有效理解其意图。现有研究主要通过引入偏离最优路径的差异化行为来传递意图，此类方案存在牺牲一部分执行效率以达到传递意图的目的，在强混淆场景下，存在以下两类难题：1）场景中路线重叠且密集，智能体通过牺牲效率的方法传递意图效果不佳。2）重叠的路线导致智能体原本的行为序列难以表征出足够的信息，观察者难以识别其真实意图。针对此类问题，探究人类在复杂场景协作中影响其行为可读性的重要因素，其不同因素在人类协作中主要表现动作姿态、速度和距离等。 本文在不改变智能体原有行为的基础上，通过对其关键状态信息如姿态、速度和距离进行多维度建模与融合，以此提出一种多维融合表征机制。设计符合人类推理的姿态映射函数、运用高斯核函数将速度和距离表征转换映射，并通过融合参数集成智能体相关表征，实现增强行为可读性的目的。此外，匹配于多维表征机制提出一种基于贝叶斯更新的高效意图识别方案，通过充分利用机制中已融合的状态信息，进行智能体意图的快速准确识别。最后，将以上机制与方案整体实现为一种新的智能体行为可读性增强与识别方法，不仅增强了整体行为对其意图的显式表达能力，而且可通过缩减差异化行为数量，进而有利于任务执行效率提升。 通过无约束、复杂障碍、强混淆三类实验场景对方法进行全面验证，实验结果表明，对比于其他基准方法：本方法加入更多维度的状态信息，使得对智能体意图识别的准确度平均提升44.47%；且本方法以更短的行为序列传达充足意图信息，极大提升了任务执行效率，使智能体执行成本平均降低42.86%；此外通过消融实验验证了姿态、速度、距离三类状态信息对可读性提升的正向作用，其中姿态信息最为显著；由敏感性分析验证了颜色与速度因子参数的最优取值区间，保障了方法的稳定性能；并通过连续控制任务实验检验了方法在复杂引擎与实际工程应用中的表现；另一方面，算法复杂度分析显示，该方法推理时间耗时对比原基底方法增加2.89毫秒，可满足实时交互需求。 本文提出的一种新的智能体行为可读性增强与识别方法提供了减少冗余行为，提高可读性的新的路径。在保有其原有行为序列的前提下，兼顾了信念传达与效率执行。该方法可以直接嵌入不同类型基准方法，并实现不同程度识别准确率提升，后续工作将围绕本文方法的可嵌入性展开，进一步发掘其在连续控制任务中的潜力。