Automations can help support the achievement of various types of goals in a smart home. We present an architecture enabling the possibility of supporting user goals consistent with the principles of a circular economy (such as energy saving and waste recycling). It includes meta-design tools for creating automations that better fit user needs and controlling their execution through innovative modalities based on conversational agents and augmented reality, and a home Digital Twin useful for supporting relevant simulations and analytics to understand the potential impact of specific automations on aspects relevant for circular economy.

Abstract The proliferation of sensors and smart Internet of Things (IoT) devices in our everyday environments is reshaping our interactions with everyday objects. This change underlines the need to empower non-expert users to easily configure the behaviour of these devices to align with their preferences and habits. At the same time, recent advances in generative transformers, such as ChatGPT, have opened up new possibilities in a variety of natural language processing tasks, enhancing reasoning capabilities and conversational interactions. This paper presents RuleBot + + , a conversational agent that exploits GPT-4 to assist the user in the creation and modification of trigger-action automations through natural language. After an introduction to motivations and related work, we present the design and implementation of RuleBot + + and report the results of the user test in which users interacted with our solution and Home Assistant, one of the most used open-source tools for managing smart environments.

In the last few years, text and voice-based conversational agents have become more and more popular all over the world as virtual assistants for a variety of tasks. In addition, the deployment on the market of many smart objects connected with these agents has introduced the possibility of controlling and personalising the behaviour of several connected objects using natural language. This has the potential to allow people, also those without a technical background, to effectively control and use the wide variety of connected objects and services. In this paper, we present an analysis of how conversational agents have been used to interact with smart environments (such as smart homes). For this purpose, we have carried out a systematic literature review considering publications selected from the ACM and IEEE digital libraries to investigate the technologies used to design and develop conversational agents for IoT settings, including Artificial Intelligence techniques, the purpose that they have been used for, and the level of user involvement in such studies. The resulting analysis is useful to better understand how this field is evolving and indicate the challenges still open in this area that should be addressed in future research work to allow people to completely benefit from this type of solution.

The spread of sensors and intelligent devices of the Internet of Things and their integration in daily environments are changing the way we interact with some of the most common objects in everyday life. Therefore, there is an evident need to provide non-expert users with the ability to customize in a simple but effective way the behaviour of these devices based on their preferences and habits. This paper presents RuleBot, a conversational agent that uses machine learning and natural language processing techniques to allow end users to create automations according to a flexible implementation of the trigger-action paradigm, and thereby customize the behaviour of devices and sensors using natural language. In particular, the paper describes the design and implementation of RuleBot, and reports on a user test and lessons learnt.