Towards Natural Cognitive System Training Interactions: A Preliminary Framework
Towards Natural Cognitive System Training Interactions: A Preliminary Framework
复制标题
走向自然认知系统训练互动：初步框架
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
K. Koedinger
中科院分区：
文献类型：
--
作者：
Erik Harpstead;Christopher Maclellan;Robert P. Marinier;K. Koedinger
研究人员已经开发出能够在复杂任务中达到人类水平的认知系统（例如沃森和AlphaGo），但构建这些系统需要大量的时间和专业知识。为了应对这一挑战，一项新的研究已经开始围绕用户可以教授而不是编程的认知系统的概念进行整合。本研究的一个关键目标是为最终用户开发直接训练这些系统执行新任务的自然方法。然而，是什么让训练互动变得自然，仍然是一个开放的研究问题，我们将在本文中开始探索。为了为这一探索奠定基础，我们回顾了人机交互文献，以确定历史上对最终用户来说是自然交互的系统特征。在此基础上，我们提出了一个认知系统训练交互的框架，该框架将交互分解为模式、类型和模式，所有这些都支持不同类型知识的获取。最后，我们讨论了这个框架如何在这个领域内的现有研究特征，以及它如何指导未来的研究。
Researchers have developed cognitive systems capable of human-level performance at complex tasks (e.g., Watson and AlphaGo), but constructing these systems required substantial time and expertise. To address this challenge, a new line of research has begun to coalesce around the concept of cognitive systems that users can teach rather than program. A key goal of this research is to develop natural approaches for end users to directly train these systems to perform new tasks. However, what makes training interactions natural remains an open research question that we begin to explore in this paper. To lay the foundation for this exploration, we review the human-computer interaction literature to identify characteristics of systems that have historically been natural for end users to interact with. Based on this review, we propose a framework for cognitive system training interactions that de-composes interaction into patterns , types , and modalities , all of which support the acquisition of different kinds of knowledge . Finally, we discuss how this framework characterizes existing research within this space and how it can guide future research.