# Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language

arXiv: https://arxiv.org/abs/2306.08014 · HTML: https://mlsanity.com/paper/2306.08014/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 13 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Expectation Propagation for approximate Bayesian inference](https://mlsanity.com/paper/1301.2294/) (2013)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [A free energy principle for a particular physics](https://mlsanity.com/paper/1906.10184/) (2019)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [Action and Perception as Divergence Minimization](https://mlsanity.com/paper/2009.01791/) (2020)
- [Reactive Message Passing for Scalable Bayesian Inference](https://mlsanity.com/paper/2112.13251/) (2021)
- [Branching Time Active Inference: the theory and its generality](https://mlsanity.com/paper/2111.11107/) (2021)
- [Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control](https://mlsanity.com/paper/2103.06859/) (2021)
- [Realising Synthetic Active Inference Agents, Part II: Variational Message Updates](https://mlsanity.com/paper/2306.02733/) (2023)

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

- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2504.14898/) (2025)
- [What Type of Inference is Active Inference?](https://mlsanity.com/paper/2606.04935/) (2026)
- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2606.20658/) (2026)
- [Sophisticated Policies from Epistemic Priors](https://mlsanity.com/paper/2607.19518/) (2026)

## Abstract (arXiv)

The Free Energy Principle (FEP) is a theoretical framework for describing how (intelligent) systems self-organise into coherent, stable structures by minimising a free energy functional. Active Inference (AIF) is a corollary of the FEP that specifically details how systems that are able to plan for the future (agents) function by minimising particular free energy functionals that incorporate information seeking components. This paper is the first in a series of two where we derive a synthetic version of AIF on free form factor graphs. The present paper focuses on deriving a local version of the free energy functionals used for AIF. This enables us to construct a version of AIF which applies to arbitrary graphical models and interfaces with prior work on message passing algorithms. The resulting messages are derived in our companion paper. We also identify a gap in the graphical notation used for factor graphs. While factor graphs are great at expressing a generative model, they have so far been unable to specify the full optimisation problem including constraints. To solve this problem we develop Constrained Forney-style Factor Graph (CFFG) notation which permits a fully graphical description of variational inference objectives. We then proceed to show how CFFG's can be used to reconstruct prior algorithms for AIF as well as derive new ones. The latter is demonstrated by deriving an algorithm that permits direct policy inference for AIF agents, circumventing a long standing scaling issue that has so far hindered the application of AIF in industrial settings. We demonstrate our algorithm on the classic T-maze task and show that it reproduces the information seeking behaviour that is a hallmark feature of AIF.