This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot.

中文翻译：

---

人机交互的自动域建模

本文介绍了一种用于人机交互的自动域建模方法。所提出的方法是象征性的，旨在用于语义不受约束的面向任务的人机交互区域。在规范级别，它是受认知启发的，解决了与自然语言人机交互有关的人类记忆系统的选定认知机制（例如，集成，语义分类，联想学习等）。我们讨论引入方法的基于语料库的验证，并报告其在与类似人的机器人集成的对话代理中的特定实现方式。