Kappas Arvid, Gratch Jonathan
Constructor University, Campus Ring 1, 28759 Bremen, Germany.
Institute for Creative Technologies, University of Southern California, Los Angeles, CA USA.
Affect Sci. 2023 Aug 18;4(3):580-585. doi: 10.1007/s42761-023-00211-3. eCollection 2023 Sep.
AI research focused on interactions with humans, particularly in the form of robots or virtual agents, has expanded in the last two decades to include concepts related to affective processes. Affective computing is an emerging field that deals with issues such as how the diagnosis of affective states of users can be used to improve such interactions, also with a view to demonstrate affective behavior towards the user. This type of research often is based on two beliefs: (1) artificial emotional intelligence will improve human computer interaction (or more specifically human robot interaction), and (2) we understand the role of affective behavior in human interaction sufficiently to tell artificial systems what to do. However, within affective science the focus of research is often to test a particular assumption, such as "smiles affect liking." Such focus does not provide the information necessary to synthesize affective behavior in long dynamic and real-time interactions. In consequence, theories do not play a large role in the development of artificial affective systems by engineers, but self-learning systems develop their behavior out of large corpora of recorded interactions. The status quo is characterized by measurement issues, theoretical lacunae regarding prevalence and functions of affective behavior in interaction, and underpowered studies that cannot provide the solid empirical foundation for further theoretical developments. This contribution will highlight some of these challenges and point towards next steps to create a rapprochement between engineers and affective scientists with a view to improving theory and solid applications.
在过去二十年中，专注于与人类互动的人工智能研究不断扩展，尤其以机器人或虚拟代理的形式出现，如今已涵盖与情感过程相关的概念。情感计算是一个新兴领域，它涉及到如何利用对用户情感状态的诊断来改善此类互动等问题，同时也旨在向用户展示情感行为。这类研究通常基于两种观点：（1）人工情感智能将改善人机交互（或更具体地说是人机机器人交互），（2）我们对情感行为在人际互动中的作用有足够的了解，能够告诉人工系统该怎么做。然而，在情感科学领域，研究重点往往是检验某个特定假设，比如“微笑影响喜爱度”。这种重点并未提供在长时间动态实时互动中合成情感行为所需的信息。因此，理论在工程师开发人工情感系统的过程中作用不大，而是自我学习系统从大量记录的互动语料库中发展出自身行为。目前的状况表现为存在测量问题、关于情感行为在互动中的普遍性和功能的理论空白，以及缺乏有力的研究，无法为进一步的理论发展提供坚实的实证基础。本论文将突出其中一些挑战，并指出下一步方向，以期在工程师和情感科学家之间达成和解，从而改进理论并实现可靠的应用。
