user adaPtive Artificial iNtelligence fOR humAn coMputer interAction

用于人机交互的用户自适应人工智能

• 批准号: 442607480
• 负责人: Professorin Dr. Elisabeth André
• 金额: --
• 依托单位: Laboratoire dInformatique pour la Mécanique et les Sciences de lIngénieur (LIMSI)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/442607480?language=en
• 关键词: user; adaPtive; Artificial; iNtelligence; humAn

The key concept of PANORAMA is "user adaptive AI in the context of human-computer interaction". First, we will conduct research on user adaptivity of Artificial Intelligence embodied as a conversational agent. When people talk to other people, they change their verbal and nonverbal communication behaviors continuously according to those of the partner. Therefore, user adaptivity is an essential issue in improving human-agent interaction. Communication style is also different depending on the culture, and adapting the agent behaviors to a target culture is required in system localization. PANORAMA will tackle these problems with Machine Learning. However, a bottleneck of this approach is that annotating users’ nonverbal behaviors to create training data is time consuming. We will solve this problem by exploiting Explainable Artificial Intelligence (XAI) technique, through which labels predicted by the system are adapted based on the interaction with the user as an annotator. Thus, user adaptive AI enables to support users in creating multimodal corpus as well as improve human-agent interaction. Moreover, user adaptivity is considered in PANORAMA via psychological theories, in which user motivation will be investigated in one relevant use case (personalised motivational coaching for physical activity). Therefore, PANORAMA envisions a new research methodology for Machine-Learning-based culture-specific conversational agents by focusing on the concept of user adaptivity.PANORAMA aims to accomplish the following five research goals. (1) propose a user adaptive multimodal annotation tool based on XAI techniques, (2) exploit this tool to collect annotated multimodal corpora in three countries (France, Germany, and Japan), (3) propose models and methods for developing conversational agents with multi-level adaptation functionality, where nonverbal signals of the agent as well as the content of the dialogue are adapted to the user, (4) provide multitask learning and transfer learning techniques to learn models using the multi-cultural corpus obtained in (2) and adapt the conversational agent to each culture, and (5) propose the design basis of adaptive AI systems grounded in psychological theories and evaluation studies.

PANORAMA的关键概念是“人机交互背景下的用户自适应人工智能”。首先，我们将研究以对话代理形式体现的人工智能的用户适应性。当人们与他人交谈时，他们会根据对方的行为不断改变自己的言语和非言语交流行为。因此，用户适应性是改善人机交互的一个重要问题。沟通方式也因文化而异，系统本地化需要使座席行为适应目标文化。 PANORAMA 将通过机器学习解决这些问题。然而，这种方法的瓶颈是注释用户的非语言行为以创建训练数据非常耗时。我们将通过利用可解释人工智能（XAI）技术来解决这个问题，通过该技术，系统预测的标签会根据与用户作为注释者的交互进行调整。因此，用户自适应人工智能能够支持用户创建多模态语料库并改善人机交互。此外，PANORAMA 通过心理学理论考虑用户适应性，其中将在一个相关用例（针对身体活动的个性化动机指导）中调查用户动机。因此，PANORAMA 通过关注用户适应性的概念，为基于机器学习的特定文化会话代理设想了一种新的研究方法。 PANORAMA 旨在实现以下五个研究目标。 （1）提出一种基于 XAI 技术的用户自适应多模态注释工具，（2）利用该工具收集三个国家（法国、德国和日本）的带注释多模态语料库，（3）提出开发具有多级自适应功能的会话代理的模型和方法，其中代理的非语言信号以及对话内容适应用户，（4）提供多任务 学习和迁移学习技术，使用（2）中获得的多文化语料库来学习模型，并使会话代理适应每种文化，（5）提出基于心理学理论和评估研究的自适应人工智能系统的设计基础。