摘要
在人机交互过程中,基于动态贝叶斯网络与领域专家知识,提出一种参与度评估方法,重点解决训练样本不足的问题.首先,对人机交互过程中的社会信号与动态贝叶斯网络的推理过程进行了描述,并建立了参与度评估模型的拓扑结构;然后,针对基于数据驱动模型参数化的局限性,通过设计语言变量集来收集专家建议,并对这些建议进行模糊化以及解模糊处理,完成了评估模型的构建;最后,通过机器人平台NAO并设计交互场景对评估模型进行了验证.实验表明,参与度评估结果与交互对象的实际行为相一致,能够使机器人在人机交互过程中正确识别出交互对象的参与意图.与已有的参与度评估方法相比,所提出的方法不受训练样本的限制,具有较高的实用价值和较强的泛化能力.
In the process of human-robot interaction, an engagement evaluation algorithm is proposed based on dynamic Bayesian network and domain expert knowledge, which focuses on solving the problem of insufficiency of training samples.Firstly, the social signals during the human-robot interaction and the reasoning process of dynamic Bayesian network are described, and the topology of the evaluation model of the engagement is developed. Then, limitations of the parameterization of the data-driven model are considered. Experts' opinions are collected by designing the linguistic variable set, and are processed by fuzzification and defuzzification. Thus, the construction of evaluation model is completed. Finally, the evaluation model is verified through the robot platform NAO and the design of interactive scenario. The experiment shows that the results of engagement evaluation are consistent with the actual behaviors of the interactive object, so that the robot can correctly identify the engagement intention of the interactive object in the process of human-robot interaction. Compared with the existing methods of engagement evaluation, the proposed method is not limited by training samples, and has high practical value and strong generalization ability.
出处
《机器人》
EI
CSCD
北大核心
2018年第2期169-177,共9页
Robot
基金
国家自然科学基金(61327806)
关键词
参与度评估
人机交互
动态贝叶斯网络
语言变量
engagement evaluation
human-robot interaction
dynamic Bayesian network
linguistic variables
