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Composable Multimodal Dialogues Based on Communicative Acts

机译:基于交际行为的可组合多模式对话

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In Social Robotics, being able to interact with users in a natural way is a key feature. To achieve this, we need to model dialogues that allow the robot to complete its tasks and to adapt to unforeseen changes in the conversation. We present an approach where these dialogues are modelled as a combination of basic interaction units, called Communicative Acts or CAs. With this, our system aims to provide all the necessary tools so each of the robot's applications can tailor their own dialogues in a simplier way. These applications make the decisions that need task-related information and request the activation of the CAs in order to create complex dialogues. The CAs handle decisions that require communication-related information (e.g. giving the user some information, or asking a question). They also manage some of the problems that can appear in any interaction, like not being able to understand the other peer, or not getting an answer to a question. A case study associated to a cognitive stimulation exercise is presented in this paper to validate our system.
机译:在社交机器人中,能够以自然方式与用户交互是一项关键功能。为此,我们需要对对话进行建模,以使机器人能够完成其任务并适应对话中无法预料的变化。我们提出了一种方法,其中将这些对话建模为基本交互单元(称为“沟通行为”或CA)的组合。以此,我们的系统旨在提供所有必要的工具,以便每个机器人的应用程序都可以以更简单的方式定制自己的对话。这些应用程序做出需要任务相关信息的决策,并要求激活CA以创建复杂的对话。 CA处理需要与通信有关的信息的决策(例如,向用户提供一些信息或提出问题)。他们还管理在任何交互中都可能出现的一些问题,例如无法理解对方,或者无法获得问题的答案。本文提出了与认知刺激运动有关的案例研究,以验证我们的系统。

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