Realising Synthetic Active Inference Agents, Part II: Variational Message Updates

• URL: http://arxiv.org/abs/2306.02733v3
• Date: Thu, 26 Sep 2024 08:45:22 GMT
• Title: Realising Synthetic Active Inference Agents, Part II: Variational Message Updates
• Authors: Thijs van de Laar, Magnus Koudahl, Bert de Vries,
• Abstract summary: Active Inference (AIF) is a corollary of the Free Energy Principle (FEP) We describe a scalable, epistemic approach to synthetic AIF, by message passing on free-form Forney-style Factor Graphs (FFGs) With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models.
• Score: 2.2940141855172036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimising an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF, by message passing on free-form Forney-style Factor Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG) notation that visually represents (generalised) FE objectives for AIF. The current paper (part II) derives message passing algorithms that minimise (generalised) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalised FE agents illustrates how the message passing approach to synthetic AIF induces epistemic behaviour on a T-maze navigation task. Extension of the T-maze simulation to 1) learning goal statistics, and 2) a multi-agent bargaining setting, illustrate how this approach encourages reuse of nodes and updates in alternative settings. With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF.

Related papers

• Free Energy Projective Simulation (FEPS): Active inference with interpretability [40.11095094521714]
  Free Energy Projective Simulation (FEP) and active inference (AIF) have achieved many successes. Recent work has focused on improving such agents' performance in complex environments by incorporating the latest machine learning techniques. We introduce Free Energy Projective Simulation (FEPS) to model agents in an interpretable way without deep neural networks.
  arXiv  Detail & Related papers  (2024-11-22T15:01:44Z)
• R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models [50.19174067263255]
  We introduce prior preference learning techniques and self-revision schedules to help the agent excel in sparse-reward, continuous action, goal-based robotic control POMDP environments. We show that our agents offer improved performance over state-of-the-art models in terms of cumulative rewards, relative stability, and success rate.
  arXiv  Detail & Related papers  (2024-09-21T18:32:44Z)
• Value of Information and Reward Specification in Active Inference and POMDPs [7.120454740315046]
  Expected free energy (EFE) is a central quantity in active inference. We show that EFE approximates the Bayes optimal RL policy via information value.
  arXiv  Detail & Related papers  (2024-08-13T00:32:05Z)
• Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models [59.331993845831946]
  Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. This paper provides the first theoretical study towards understanding the influence of guidance on diffusion models in the context of Gaussian mixture models.
  arXiv  Detail & Related papers  (2024-03-03T23:15:48Z)
• Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations [56.941276017696076]
  We propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP) CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.
  arXiv  Detail & Related papers  (2023-09-09T04:05:56Z)
• Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations [61.132408427908175]
  zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain. With only a single representative text feature instead of real images, the synthesized images gradually lose diversity. We propose a novel method to find semantic variations of the target text in the CLIP space.
  arXiv  Detail & Related papers  (2023-08-21T08:12:28Z)
• Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language [2.5782420501870296]
  This paper is the first in a series of two where we derive a synthetic version of Active Inference on free form factor graphs. We develop Constrained Forney-style Factor Graph notation which permits a fully graphical description of variational inference objectives. We derive an algorithm that permits direct policy inference for AIF agents, circumventing a long standing scaling issue.
  arXiv  Detail & Related papers  (2023-06-13T11:57:52Z)
• AFAFed -- Protocol analysis [3.016628653955123]
  This is a novel A Fair Federated Adaptive learning framework for stream-oriented IoT application environments. We analyze the convergence properties and address the implementation aspects AFAFed.
  arXiv  Detail & Related papers  (2022-06-29T22:12:08Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Active Inference and Epistemic Value in Graphical Models [3.9457043990895904]
  The Free Energy Principle (FEP) postulates that biological agents perceive and interact with their environment in order to minimize a Variational Free Energy (VFE) with respect to a generative model of their environment. This paper approaches epistemic behavior from a constrained Bethe Free Energy (CBFE) perspective. We illustrate resulting behavior of the CBFE by planning and interacting with a simulated T-maze environment.
  arXiv  Detail & Related papers  (2021-09-01T16:43:35Z)
• FedDANE: A Federated Newton-Type Method [49.9423212899788]
  Federated learning aims to jointly learn low statistical models over massively distributed datasets. We propose FedDANE, an optimization that we adapt from DANE, to handle federated learning.
  arXiv  Detail & Related papers  (2020-01-07T07:44:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.