生成类似人类的运动：基于环境特征的两种方法的比较(CS)

A. 宗塔， S.K. 斯密特， A.E. 艾本

模拟中逼真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，由意图（如购买杂货）和周围环境（例如好奇地看到有趣的新地方）所驱动。在线和离线提供的服务在规划路径时通常不会考虑环境，尤其是在休闲旅行中。有两种新颖的算法提出，以基于环境特征生成人样轨迹。基于吸引力的 A* 算法在计算信息中包括环境特征，同时，基于特征的 A* 算法在计算中还注入了来自真实轨迹的信息。人类相似方面已经由一位人类专家测试，认为最终产生的轨迹是现实的。本文在效率、功效和超参数灵敏度等关键指标中对两种方法进行比较。尽管根据我们预定义的指标生成更接近真实的轨迹，但我们将展示，与基于吸引力的 A* 算法相比，基于特征的 A* 算法在时间效率上如何不足，而这阻碍了模型在现实世界中的可用性。

Generating Human-Like Movement: A Comparison Between Two Approaches Based on Environmental Features

A. Zonta, S.K. Smit, A.E. Eiben

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.