认识人类的内部状态：基于概念的方法
Recognizing Human Internal States: A Conceptor-Based Approach
摘要：
在过去的几十年里，人们对社交机器人作为行为教练应用于自闭症谱系障碍干预的兴趣越来越大[4]。我们认为，嵌入治疗中的机器人也可以通过观察患者的行为来提供定量诊断信息。ASD症状的社会性质意味着，为了实现这一目标，机器人需要能够识别其人类互动伙伴正在经历的内部状态，例如混乱，参与等状态。（1）机器人可以获得的哪些信息可以用来识别内部状态，以及（2）系统如何对内部状态进行分类，以便提供足够详细的诊断信息？在本文中，我们深入讨论这两个问题，并提出了一种新的，基于概念的分类器。我们报告的初步结果，该系统的概念验证研究和未来的工作计划大纲。
英文摘要：
The past few decades has seen increased interest in the application of social robots to interventions for Autism Spectrum Disorder as behavioural coaches [4]. We consider that robots embedded in therapies could also provide quantitative diagnostic information by observing patient behaviours. The social nature of ASD symptoms means that, to achieve this, robots need to be able to recognize the internal states their human interaction partners are experiencing, e.g. states of confusion, engagement etc. Approaching this problem can be broken down into two questions: (1) what information, accessible to robots, can be used to recognize internal states, and (2) how can a system classify internal states such that it allows for sufficiently detailed diagnostic information? In this paper we discuss these two questions in depth and propose a novel, conceptor-based classifier. We report the initial results of this system in a proof-of-concept study and outline plans for future work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
