International audience

Abstract In Ambient Intelligence (AmI), seamless interaction between humans and AI systems is crucial for the effective utilization of smart environments. This paper investigates human–AI interaction in AmI, focusing on the management of uncertainty—situations where the AI is unsure how to choose between multiple possible outputs—within the context of Opportunistic Software Composition. The Opportunistic Composition Engine (OCE) dynamically constructs applications or assemblies from available software components, learning from human feedback to tailor applications to user preferences and context. We design, implement, and evaluate three approaches to uncertainty handling: explicitly asking the user to resolve uncertainty, offering a choice between multiple assemblies, and using a visual gradient to indicate the AI’s confidence. Each approach is implemented in a distinct version of OCE and assessed through a user study ( N = 121) measuring usability and learning performance in a 2D online simulated ambient environment. Our results show that providing clear visual or informational cues about uncertainty improves user satisfaction, while forcing users to resolve uncertainty themselves reduces usability and can hinder learning. Importantly, the other interaction strategies do not impair the system’s learning, suggesting that lightweight, system-guided handling of uncertainty can effectively support both user experience and AI adaptation. Beyond these conclusions, this work demonstrates the utility and general applicability of simulation-based technologies for prototyping and evaluating AmI applications.

As the vision of Ambient Intelligence (AmI) becomes more feasible, the challenge of designing effective and usable human-machine interaction in this context becomes increasingly important. Interactive Machine Learning (IML) offers a set of techniques and tools to involve end-users in the machine learning process, making it possible to build more trustworthy and adaptable ambient systems. In this paper, our focus is on exploring approaches to effectively integrate and assist human users within ML-based AmI systems. Through a survey of key IML-related contributions, we identify principles for designing effective human-AI interaction in AmI applications. We apply them to the case of Opportunistic Composition, which is an approach to achieve AmI, to enhance collaboration between humans and Artificial Intelligence. Our study highlights the need for user-centered and context-aware design, and provides insights into the challenges and opportunities of integrating IML techniques into AmI systems.

As the vision of Ambient Intelligence (AmI) becomes more feasible, the challenge of designing effective and usable human-machine interaction in this context becomes increasingly important. Interactive Machine Learning (IML) offers a set of techniques and tools to involve end-users in the machine learning process, making it possible to build more trustworthy and adaptable ambient systems. In this paper, our focus is on exploring approaches to effectively integrate and assist human users within ML-based AmI systems. Through a survey of key IML-related contributions, we identify principles for designing effective human-AI interaction in AmI applications. We apply them to the case of Opportunistic Composition, which is an approach to achieve AmI, to enhance collaboration between humans and Artificial Intelligence. Our study highlights the need for user-centered and context-aware design, and provides insights into the challenges and opportunities of integrating IML techniques into AmI systems.