论文标题
产生类似人类的运动：基于环境特征的两种方法之间的比较
Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features
论文作者
论文摘要
在模拟中建模现实的人类行为是一个持续的挑战，它存在于社会科学，哲学和人工智能等几个领域之间。人类运动是一种特殊的行为类型，是由意图驱动的（例如获得杂货）和周围环境（例如，看到新有趣的地方的好奇心）。在计划路径时，在线和离线服务通常不会考虑环境，这是决定性的，尤其是在休闲旅行中。已经提出了两种新型算法，以基于环境特征产生类似人类的轨迹。同时，基于吸引力的A*算法包括来自环境功能的计算信息，基于功能的A*算法还会在其计算中注入来自实际轨迹的信息。人类风格的方面已由人类专家进行了测试，该专家认为最终产生的轨迹是现实的。本文在某些关键指标（例如效率，功效和超参数敏感性）中进行了比较。我们展示了如何根据我们预定义的指标生成更接近真实的轨迹，但与基于吸引人的A*算法相比，基于功能的A*算法的时间效率短，从而阻碍了现实世界中模型的可用性。
Modelling realistic human behaviours in simulation is an ongoing challenge that resides between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider the environment when planning a path, which is decisive especially on a leisure trip. Two novel algorithms have been presented to generate human-like trajectories based on environmental features. The Attraction-Based A* algorithm includes in its computation information from the environmental features meanwhile, the Feature-Based A* algorithm also injects information from the real trajectories in its computation. The human-likeness aspect has been tested by a human expert judging the final generated trajectories as realistic. This paper presents a comparison between the two approaches in some key metrics like efficiency, efficacy, and hyper-parameters sensitivity. We show how, despite generating trajectories that are closer to the real one according to our predefined metrics, the Feature-Based A* algorithm fall short in time efficiency compared to the Attraction-Based A* algorithm, hindering the usability of the model in the real world.
