# Active Inference and Human--Computer Interaction

arXiv: https://arxiv.org/abs/2412.14741 · HTML: https://mlsanity.com/paper/2412.14741/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 165 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Optimal Policies Tend to Seek Power](https://mlsanity.com/paper/1912.01683/) (2019)
- [RecSim: A Configurable Simulation Platform for Recommender Systems](https://mlsanity.com/paper/1909.04847/) (2019)
- [Predictive Coding: a Theoretical and Experimental Review](https://mlsanity.com/paper/2107.12979/) (2021)
- [A Survey of Exploration Methods in Reinforcement Learning](https://mlsanity.com/paper/2109.00157/) (2021)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Bayesian Workflow](https://mlsanity.com/paper/2011.01808/) (2020)
- [Whence the Expected Free Energy?](https://mlsanity.com/paper/2004.08128/) (2020)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [The Simpson's Paradox in the Offline Evaluation of Recommendation Systems](https://mlsanity.com/paper/2104.08912/) (2021)
- [Amortized Variational Inference: A Systematic Review](https://mlsanity.com/paper/2209.10888/) (2022)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [How does HCI Understand Human Autonomy and Agency?](https://mlsanity.com/paper/2301.12490/) (2023)
- [Learning to Assist Humans without Inferring Rewards](https://mlsanity.com/paper/2411.02623/) (2024)
- [Exploration and preference satisfaction trade-off in reward-free learning](https://mlsanity.com/paper/2106.04316/) (2021)
- [Prior Preference Learning from Experts:Designing a Reward with Active Inference](https://mlsanity.com/paper/2101.08937/) (2021)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop](https://mlsanity.com/paper/1806.08083/) (2018)
- [An active inference model of collective intelligence](https://mlsanity.com/paper/2104.01066/) (2021)
- [Free Energy in a Circumplex Model of Emotion](https://mlsanity.com/paper/2407.02474/) (2024)
- [A Probabilistic Interpretation of Motion Correlation Selection Techniques](https://mlsanity.com/paper/2102.03955/) (2021)
- [The Role of Higher-Order Cognitive Models in Active Learning](https://mlsanity.com/paper/2401.04397/) (2024)

## Built on by (most cited first, 4 of 4)

- [Know You Before You Speak: User-State Modeling for LLM Personalization in Multi-Turn Conversation](https://mlsanity.com/paper/2605.24647/) (2026)
- [An Active Inference Model of Mouse Point-and-Click Behaviour](https://mlsanity.com/paper/2510.14611/) (2025)
- [MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning](https://mlsanity.com/paper/2602.15245/) (2026)
- [BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference](https://mlsanity.com/paper/2609.26021/) (2026)

## Abstract (arXiv)

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.