在第一人称牧羊任务中建模人类导航和决策动态
Ayman Bin Kamruddin1,2, Hannah Sandison3, Gaurav Patil3
1Modeling and Engineering Risk and Complexity, Scuola Superiore Meridionale, Naples, Italy.
Royal Society open science
|October 31, 2024
概括
动态感知运动原始体 (DPMPs) 有效地模拟了在牧羊游戏中的人类导航和目标选择. 这种方法准确地模拟和预测了复杂场景中的玩家行为.
科学领域:
- 认知科学 认知科学
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
背景情况:
- 了解人类的导航和决策对于开发直观的人机界面至关重要.
- 动态感知运动原始体 (DPMPs) 为模拟复杂的运动行为提供了一个框架.
研究的目的:
- 调查DPMPs在第一人称牧羊任务中捕捉人类导航和目标选择的适用性.
- 评估组合DPMP和启发式政策模型对人类行为的预测能力.
主要方法:
- 人类参与者参与了第一人称的牧羊游戏,将虚拟目标排列在一起.
- 运动轨迹和目标选择决策被记录并使用DPMP和启发式策略建模.
主要成果:
- 一个DPMP导航模型准确地复制了参与者的运动轨迹.
- 一个启发式策略捕获了大约80%的目标选择决策.
- 组合模型成功模拟并预测了新背景下的行为动态.
结论:
- DPMP提供了一个强大的框架来建模复杂的人类感知运动行为,包括导航和决策.
- 将DPMP与启发式策略集成,可以提高对人类行为的模拟和预测.
- 这些发现对设计用于人机交互的先进人工代理产生影响.
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