为了使政策解释的可用性和实用性与连续决策系统的可用性相协调
Pradyumna Tambwekar1, Matthew Gombolay1
1School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States.
Frontiers in robotics and AI
|August 6, 2024
概括
在安全关键领域的人类-人工智能协作通过了解自主代理政策来改进. 这项研究揭示了一个权衡:文本解释更易于使用,但决策树为强化学习代理提供了更好的客观理解.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 安全关键领域越来越多地使用自主代理进行连续决策.
- 强化学习 (RL) 代理人学习政策,但患有信用分配问题,使得失败难以诊断.
- 有效的人类-人工智能协作需要可理解的代理政策来预测行为和减轻故障.
研究的目的:
- 为RL代理人确定影响政策解释感知可用性和客观有用性的因素.
- 调查解释模式和初始代理印象对用户理解的影响.
- 解决在关键应用中对人类依赖和自主系统的合规性方面的挑战.
主要方法:
- 实验对象包括两个因素:解释方式 (树,文本,修改文本,程序) 和代理人的第一印象 (成功/失败).
- 测量了参与者的感知可用性和预测代理行为的客观表现.
- 分析了特定用户 (如计算机科学经验) 和情境因素的影响.
主要成果:
- 观察到一种偏好性能的权衡:基于语言的解释 (文本,修改文本) 被认为更易于使用.
- 决策树解释导致了对代理行为的更好的客观预测.
- 用户体验和目击代理失败显著影响了解释的感知和实用性.
结论:
- 解释模式和用户/情境因素极大地影响RL代理政策解释的可用性和有用性.
- 结果为开发更好地解释AI (XAI) 系统提供了洞察力,特别是在安全关键的应用中.
- 这项研究指导XAI开发人员创建更有效的政策解释,以改善人类-AI合作并减少不适当的依赖.
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