设计和评估CLAICA的经验教训:一个持续学习的人工智能认知助手

Samuel Kernan Freire, E. Niforatos, Chaofan Wang, Santiago Ruiz-Arenas, Mina Foosherian, S. Wellsandt, A. Bozzon
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引用次数: 0

摘要

学习操作一个复杂的系统,比如一条灵活的生产线,可能是一项艰巨的任务。产品的高度可变性和频繁的重新配置使得保持文档更新和在工厂工人之间分享新知识变得困难。我们介绍了CLAICA,这是一款持续学习的人工智能认知助手,可以为上述场景中的工作人员提供支持。CLAICA向(有经验的)工人学习,将新知识形式化,将其与上下文信息一起存储在知识库中,并在相关时共享。我们对83名参与者进行了一项用户研究,他们通过CLAICA完成了8项知识交换任务,完成了一项调查,并提供了定性反馈。我们的研究结果更深入地了解了先前的培训、上下文专业知识和交互方式如何影响认知助手的用户体验。我们利用我们的结果来引出认知助手的设计和评估指南,这些助手支持在快节奏和苛刻的环境中进行知识交换,例如敏捷生产线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lessons Learned from Designing and Evaluating CLAICA: A Continuously Learning AI Cognitive Assistant
Learning to operate a complex system, such as an agile production line, can be a daunting task. The high variability in products and frequent reconfigurations make it difficult to keep documentation up-to-date and share new knowledge amongst factory workers. We introduce CLAICA, a Continuously Learning AI Cognitive Assistant that supports workers in the aforementioned scenario. CLAICA learns from (experienced) workers, formalizes new knowledge, stores it in a knowledge base, along with contextual information, and shares it when relevant. We conducted a user study with 83 participants who performed eight knowledge exchange tasks with CLAICA, completed a survey, and provided qualitative feedback. Our results provide a deeper understanding of how prior training, context expertise, and interaction modality affect the user experience of cognitive assistants. We draw on our results to elicit design and evaluation guidelines for cognitive assistants that support knowledge exchange in fast-paced and demanding environments, such as an agile production line.
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