Active Inference and Human--Computer Interaction (2412.14741v1)

Abstract: 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.

• The paper presents a novel active inference framework that models human-computer interaction as a dynamic process using probabilistic generative models.
• The approach simulates both offline and real-time user behavior to optimize interface designs and adapt system responses to user needs.
• The framework highlights challenges such as computational complexity and revised evaluation metrics, paving the way for adaptive, user-centered systems.

Active Inference and Human-Computer Interaction: An Analytical Overview

The paper "Active Inference and Human–Computer Interaction" proposes a novel framework for understanding and modeling the interaction between humans and computers using the concept of Active Inference (AIF). This approach emphasizes the importance of viewing human-computer interactions as dynamic processes involving agents with internal probabilistic generative models. The paper presents an extensive review of AIF as a theoretical basis for agent behavior and discusses its potential to revolutionize the design and understanding of human-computer interaction (HCI).

Conceptual Foundation

Active Inference serves as a closed-loop computational theory grounded in the Free Energy Principle, suggesting that agents—whether biological or artificial—act to minimize their surprise about the environment. The methodology posits that agents have internal models that predict sensations resulting from actions and continually update their beliefs to align with environmental inputs. This framework allows for adaptive behavior by balancing predictions about future states with current sensory inputs.

In the context of HCI, AIF offers a paradigm where both the human and the computer are seen as agents interacting through a shared environment. This interaction is mediated by a defined set of states known as the Markov blanket, which serves to demarcate the sensory and active boundaries between the agent and its environment.

Applications in HCI

The paper thoroughly investigates how the principles of AIF can be employed within HCI to design systems that can better adapt to user needs and contexts. It lays out various potential application modes:

1. Offline Simulation: Through the development of AIF models, it becomes feasible to simulate user behaviors and optimize interface designs without direct user involvement. This can be crucial in the early stages of design where direct empirical testing may be infeasible.
2. Real-time Interaction: AIF models can inform systems that adapt dynamically to user interactions, enabling smoother and more intuitive experiences. Systems built on AIF principles can anticipate user actions and adjust in real-time, providing personalized experiences that can accommodate varying user abilities and preferences.
3. Mutual Interaction Models: The paper suggests a mutual interaction scenario where both user and system are modeled as AIF agents, creating a cohesive framework for understanding complex interactive behaviors as emergent properties of interacting probabilistic models.

Challenges and Future Directions

Implementing AIF in HCI involves notable challenges, such as computational complexity due to real-time model updating and execution, the necessity for robust generative models that accurately reflect user behavior, and the development of new preference-learning mechanisms to align system behavior with user goals effectively. Additionally, the shift to design based on probabilistic models requires a reevaluation of traditional HCI evaluation and measurement metrics to account for uncertainty and model-based predictions.

The authors posit that Active Inference offers a substantial opportunity to redefine interaction paradigms by making them more adaptive and user-centered. This aligns with broader trends in AI development focused on creating systems that can learn and adapt from their interactions with humans and their environments in meaningful ways.

Conclusion

By introducing the concept of Active Inference to HCI, the authors present a rich theoretical framework for understanding and designing interactive systems. This approach encourages a holistic view of human-computer interactions as dynamic, adaptive processes driven by probabilistic reasoning and active updating of beliefs and actions. The paper underscores the potential impact of AIF in creating more intelligent, responsive, and user-friendly interactions, though significant work remains in refining these models for practical application.