认知科学
认知
感知
动作(物理)
社会认知
计算机科学
任务(项目管理)
计算模型
社会认知
心理学
人机交互
认知心理学
人工智能
物理
管理
量子力学
神经科学
经济
作者
Sebastian Kahl,Stefan Kopp
标识
DOI:10.1098/rstb.2021.0474
摘要
It is increasingly important for technical systems to be able to interact flexibly, robustly and fluently with humans in real-world scenarios. However, while current AI systems excel at narrow task competencies, they lack crucial interaction abilities for the adaptive and co-constructed social interactions that humans engage in. We argue that a possible avenue to tackle the corresponding computational modelling challenges is to embrace interactive theories of social understanding in humans. We propose the notion of socially enactive cognitive systems that do not rely solely on abstract and (quasi-)complete internal models for separate social perception, reasoning and action. By contrast, socially enactive cognitive agents are supposed to enable a close interlinking of the enactive socio-cognitive processing loops within each agent, and the social-communicative loop between them. We discuss theoretical foundations of this view, identify principles and requirements for according computational approaches, and highlight three examples of our own research that showcase the interaction abilities achievable in this way. This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.
科研通智能强力驱动
Strongly Powered by AbleSci AI