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A collaborative behavior-based approach for handling ambiguity, uncertainty, and vagueness in robot natural language interfaces

机译:基于协作行为的方法,用于处理机器人自然语言界面中的歧义,不确定性和模糊性

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Service robots are used by ordinary people in their houses and offices. For such users, a desirable way to communicate with robots is through natural language interfaces. So far, techniques for developing robot natural language interfaces are far from mature. A major challenge is handling ambiguity, uncertainty, and vagueness in parsing, object resolution, and vague natural language words. In this research, we develop a new approach called the collaborative behavior-based approach, in which behaviors of robots and behaviors of human users, as well as the changes of object states caused by the behaviors, are taken into consideration integratedly in processing natural language user instructions. In this paper, we analyze the special features of a human-robot interface that may affect language understanding, describe our approach that is designed based on the features, and present the implementation and some experimental results.
机译:服务机器人由普通人在其房屋和办公室中使用。对于此类用户,与机器人通信的一种理想方式是通过自然语言界面。到目前为止,用于开发机器人自然语言界面的技术还很不成熟。一个主要的挑战是在解析,对象解析和模糊的自然语言单词中处理歧义,不确定性和模糊性。在这项研究中,我们开发了一种新方法,称为基于协作行为的方法,该方法在处理自然语言时综合考虑了机器人的行为和人类用户的行为以及由行为引起的对象状态的变化。用户说明。在本文中,我们分析了可能会影响语言理解的人机界面的特殊功能,描述了基于这些功能设计的方法,并给出了实现和一些实验结果。

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