通过对比的指导在解释性人机器人对话中对人类合作伙伴进行支架
André Groß1,2, Amit Singh3, Ngoc Chi Banh4
1Medical Assistance Systems, Medical School OWL, Bielefeld University, Bielefeld, Germany.
Frontiers in robotics and AI
|November 15, 2023
概括
人机交互解释中的否定可以通过减少错误和增加手势准确性来提高任务性能. 然而,它最初增加了处理时间,这种效果随着重复的指令而减少.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 语言学的语言学.
背景情况:
- 解释对人工智能系统至关重要,但系统的理解策略是不发达的.
- 否定,一种创造对比的语言工具,丰富了解释,但在人机交互方面有未知的影响.
- 以前的研究表明,否定可以单独增加认知负载,但在上下文中有助于理解.
研究的目的:
- 研究否定作为解释性人机对话中的指导策略.
- 评估否定与肯定对处理成本和任务性能的影响.
- 探索重复如何影响否定在人机器人解释的影响.
主要方法:
- 一项实证研究涉及一个虚拟机器人通过触摸屏上的手势解释任务.
- 在机器人解释中否定和肯定策略的比较.
- 测量处理成本 (反应时间) 和任务性能 (手势准确性,尝试,与原型相似).
主要成果:
- 否定增加了处理成本 (反应时间),但提高了任务性能,减少了尝试,增加了手势相似性.
- 最初正确的手势执行没有显著差异,但需要更少的否定尝试.
- 参与者对对比 (否定) 解释的评价更高;重复的指令减少了否定的影响.
结论:
- 否定可以是人机交互解释的有效策略,尽管初始处理成本,但可以提高理解和性能.
- 否定的好处随着重复暴露而减少,这表明适应性学习或习惯.
- 需要对AI解释中否定的语言处理效应进行进一步的研究.
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