mlsanityData to 7 Oct 2026

Toward Design of Synthetic Active Inference Agents by Mere Mortals

arXiv 2307.14145 ↗First posted 26 Jul 2023stat.ML cs.LG cs.NEcited by 3

Not on the map. Why?

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.

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

Builds on

7 of 17 references.

References · 7

  1. 2020Sophisticated Inferencecited by 56
  2. 2022pymdp: A Python library for active inference in discrete state spacescited by 39
  3. 2022Designing Ecosystems of Intelligence from First Principlescited by 23
  4. 2018Bayesian model reductioncited by 19
  5. 2021Reactive Message Passing for Scalable Bayesian Inferencecited by 16
  6. 2018A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithmscited by 11
  7. 2022Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimizationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.