RADAR: automated task planning for proactive decision support
RADAR: automated task planning for proactive decision support
复制标题
雷达：用于主动决策支持的自动化任务规划
DOI：
10.1080/07370024.2020.1726751
复制
发表时间：
2020-03-19
影响因子：
5.3
通讯作者：
Kambhampati, Subbarao
中科院分区：
文献类型：
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作者：
Grover, Sachin;Sengupta, Sailik;Kambhampati, Subbarao
主动决策支持旨在通过提高决策质量和决策简易性来改善人类决策者的决策体验。鉴于人工智能技术可以有效地搜索潜在的巨大解决方案空间（决策）并找到良好的解决方案，因此它可以用于需要自然决策的灾难响应等人机交互场景。在这种情况下，人类决策者可能会经历高度认知超载，从而导致情境意识丧失。在本文中，我们建议使用自动化任务规划技术以及人机交互（HCI）社区中提出的设计原则来开发主动决策支持系统。在这个意义上，我们强调了此类系统雷达的功能，并简要描述了自动化规划技术如何帮助我们提供不同程度的帮助。为了评估不同功能的有效性，我们在合成环境中对人类受试者进行消融研究，以制定交互式研究计划。我们发现，与没有任何支持的专家规划人员相比，计划验证和建议等规划技术有助于减少规划时间（客观指标）并提高用户满意度（主观指标）。
Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.