主动动态探索：将心理模拟建模为用于寻路过程中感觉运动控制的粒子滤波
Active Dynamical Prospection: Modeling Mental Simulation as Particle Filtering for Sensorimotor Control during Pathfinding
摘要：
当人类面临一个共同的挑战时，我们会怎么做：我们知道我们想去哪里，但我们还不确定到达那里的最佳方式，或者甚至我们是否可以。这是在空间导航和寻路过程中对智能体提出的问题，它的解决方案可以为我们提供关于更抽象的规划领域的线索。在这项工作中，我们在一个连续的，明确的探索性范式中建模寻路行为。在我们的任务中，参与者（和代理）必须在部分可观察的环境中协调视觉探索和导航。我们的贡献有三个主要组成部分：1）行为数据的分析，从81个人类参与者在一个新的寻路范式进行在线实验，2）一个建议，以模拟前瞻性的心理模拟在导航作为粒子滤波，和3）在计算代理这个建议的实例。我们表明，我们的模型，主动动态勘探，表现出类似的模式的地图解决方案的速度，路径选择，和审判的持续时间，以及注意力的行为（在聚合和个人水平）相比，从人类参与者的数据。我们还发现，远端注意力和延迟之前的第一次移动（前瞻性模拟的潜在相关）的任务性能的预测。
英文摘要：
What do humans do when confronted with a common challenge: we know where we want to go but we are not yet sure the best way to get there, or even if we can. This is the problem posed to agents during spatial navigation and pathfinding, and its solution may give us clues about the more abstract domain of planning in general. In this work, we model pathfinding behavior in a continuous, explicitly exploratory paradigm. In our task, participants (and agents) must coordinate both visual exploration and navigation within a partially observable environment. Our contribution has three primary components: 1) an analysis of behavioral data from 81 human participants in a novel pathfinding paradigm conducted as an online experiment, 2) a proposal to model prospective mental simulation during navigation as particle filtering, and 3) an instantiation of this proposal in a computational agent. We show that our model, Active Dynamical Prospection, demonstrates similar patterns of map solution rate, path selection, and trial duration, as well as attentional behavior (at both aggregate and individual levels) when compared with data from human participants. We also find that both distal attention and delay prior to first move (both potential correlates of prospective simulation) are predictive of task performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
