# Toward Design of Synthetic Active Inference Agents by Mere Mortals

arXiv: https://arxiv.org/abs/2307.14145 · HTML: https://mlsanity.com/paper/2307.14145/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [Designing Ecosystems of Intelligence from First Principles](https://mlsanity.com/paper/2212.01354/) (2022)
- [Bayesian model reduction](https://mlsanity.com/paper/1805.07092/) (2018)
- [Reactive Message Passing for Scalable Bayesian Inference](https://mlsanity.com/paper/2112.13251/) (2021)
- [A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms](https://mlsanity.com/paper/1811.03407/) (2018)
- [Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization](https://mlsanity.com/paper/2210.09134/) (2022)

## Built on by (most cited first, 3 of 3)

- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2504.14898/) (2025)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2606.20658/) (2026)

## Abstract (arXiv)

The theoretical properties of active inference agents are impressive, but how do we realize effective agents in working hardware and software on edge devices? This is an interesting problem because the computational load for policy exploration explodes exponentially, while the computational resources are very limited for edge devices. In this paper, we discuss the necessary features for a software toolbox that supports a competent non-expert engineer to develop working active inference agents. We introduce a toolbox-in-progress that aims to accelerate the democratization of active inference agents in a similar way as TensorFlow propelled applications of deep learning technology.