Active Inference and Human--Computer Interaction
This work addresses the challenge of improving human-computer interaction for designers and researchers by offering a new theoretical framework, though it is incremental as it reviews and adapts an existing computational theory to this domain.
The paper tackles the problem of modeling human-computer interaction by applying Active Inference, a computational framework based on probabilistic generative models, to provide a coherent basis for understanding behavior, designing systems, and measuring concepts like agency and engagement.
Active Inference is a closed-loop computational theoretical basis for understanding behaviour, based on agents with internal probabilistic generative models that encode their beliefs about how hidden states in their environment cause their sensations. We review Active Inference and how it could be applied to model the human-computer interaction loop. Active Inference provides a coherent framework for managing generative models of humans, their environments, sensors and interface components. It informs off-line design and supports real-time, online adaptation. It provides model-based explanations for behaviours observed in HCI, and new tools to measure important concepts such as agency and engagement. We discuss how Active Inference offers a new basis for a theory of interaction in HCI, tools for design of modern, complex sensor-based systems, and integration of artificial intelligence technologies, enabling it to cope with diversity in human users and contexts. We discuss the practical challenges in implementing such Active Inference-based systems.