Today users can interact with popular virtual assistants such as Siri to accomplish their tasks on a digital environment. In these systems, links between natural language requests and their concrete realizations are specified at the conception phase. A more adaptive approach would be to allow the user to provide natural language instructions or demonstrations when a task is unknown by the assistant. An adaptive solution should allow the virtual assistant to operate a much larger digital environment composed of multiple application domains and providers and better match user needs. We have previously developed robotic systems, inspired by human language developmental studies, that provide such a usage-based adaptive capacity. Here, we extend this approach to human interaction with a virtual assistant that can first learn the mapping between verbal commands and basic action semantics of a specific domain. Then, it can learn higher level mapping by combining previously learned procedural knowledge in interaction with the user. The flexibility of the system is demonstrated as the virtual assistant can learn actions in new domains (e-mail, Wikipedia, etc.), and then can learn how e-mail and Wikipedia basic procedures can be combined to form hybrid procedural knowledge.

中文翻译：

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使用自适应虚拟助手进行基于使用的人类交互学习

如今，用户可以与 Siri 等流行的虚拟助手进行交互，以在数字环境中完成他们的任务。在这些系统中，自然语言请求与其具体实现之间的联系是在概念阶段指定的。一种更具适应性的方法是允许用户在助手未知任务时提供自然语言指令或演示。自适应解决方案应允许虚拟助手操作由多个应用程序域和提供商组成的更大的数字环境，并更好地满足用户需求。我们之前在人类语言发展研究的启发下开发了机器人系统，可提供这种基于使用的自适应能力。这里，我们将这种方法扩展到与虚拟助手的人类交互，该虚拟助手可以首先学习特定领域的口头命令和基本动作语义之间的映射。然后，它可以通过结合先前学习的过程知识与用户交互来学习更高级别的映射。该系统的灵活性体现为虚拟助手可以学习新领域（电子邮件、维基百科等）中的动作，然后可以学习如何将电子邮件和维基百科基本程序结合起来形成混合程序知识。