Internet-of-Things has reshaped the way people interact with their surroundings and automatize the once manual actions. In a smart home, controlling the Internet-connected lights is as simple as speaking to a nearby conversational assistant. However, specifying interaction rules, such as making the lamp turn on at specific times or when someone enters the space is not a straightforward task. The complexity of doing such increases as the number and variety of devices increases, along with the number of household members. Thus, managing such systems becomes a problem, including finding out why something has happened. This issue lead to the birth of several low-code development solutions that allow users to define rules to their systems, at the cost of discarding the easiness and accessibility of voice interaction. In this paper we extend the previous published work on Jarvis [1], a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept is presented, detailing its architecture and natural language processing capabilities. A feasibility experiment was carried with mostly non-technical participants, providing evidence that Jarvis is intuitive enough to be used by common end-users, with participants showcasing an overall preference by conversational assistants over visual low-code solutions.

物联网重塑了人们与周围环境互动的方式，并使曾经的手动操作实现了自动化。在智能家居中，控制与Internet连接的灯光就像与附近的对话助手通话一样简单。但是，指定交互规则，例如在特定时间或在有人进入该空间时打开灯，并不是一件容易的事。这样做的复杂性随着设备数量和种类的增加以及家庭成员的数量而增加。因此，管理这样的系统成为一个问题，包括找出发生某些事情的原因。这个问题导致了几种低代码开发解决方案的诞生，这些解决方案允许用户为自己的系统定义规则，但代价是舍弃了语音交互的简便性和可访问性。在本文中，我们扩展了先前在Jarvis [1]上发表的工作，Jarvis是一种用于管理IoT系统的对话界面，该界面试图通过允许用户指定基于时间的规则，使用上下文感知进行更自然的交互，提供事件管理和解决事件来解决这些问题。支持因果关系查询。提出了概念验证，详细介绍了其体系结构和自然语言处理能力。大多数非技术人员进行了可行性实验，提供了证据，证明贾维斯足够直观，可供普通最终用户使用，参与者展示了对话助手对视觉低码解决方案的总体偏爱。使用上下文意识进行更自然的交互，提供事件管理并支持因果关系查询。提出了概念验证，详细介绍了其体系结构和自然语言处理能力。大多数非技术人员进行了可行性实验，提供了证据，证明贾维斯足够直观，可供普通最终用户使用，参与者展示了对话助手对视觉低码解决方案的总体偏爱。使用上下文意识进行更自然的交互，提供事件管理并支持因果关系查询。提出了概念验证，详细介绍了其体系结构和自然语言处理能力。大多数非技术人员进行了可行性实验，提供了证据，证明贾维斯足够直观，可供普通最终用户使用，参与者展示了对话助手对视觉低码解决方案的总体偏爱。