Active Inference in Julia

Active Inference in Julia

Overview

This skill provides a complete Active Inference implementation in Julia, demonstrating Bayesian belief updating, variational free energy calculation, and expected free energy-based policy selection.

Core Algorithms

1. Belief Updating: Bayesian inference using observation likelihoods to update posterior beliefs
2. Free Energy Calculation: KL divergence between posterior beliefs and prior distribution
3. Policy Selection: Softmax action selection over expected free energy per action
4. Perception-Action Loop: Iterative sense → infer → act cycle with generative model

Key Files

• Clone_Julia_Repo.jl — Source implementation
• Julia_InferAnts.jl — Source implementation
• RxInfer.jl — Source implementation
• RxInfer_Agent_Model.jl — Source implementation
• run.sh — Execution script (handles compilation if needed)
• README.md — Usage documentation and requirements

Usage

cd 0_CONTEXT/Computer_Languages/Julia/
./run.sh

Language-Specific Features

• Native matrix/array operations
• Scientific computing ecosystem
• Built-in visualization support

Integration

• Tested via master_controller.py test julia
• Benchmarked via benchmark_suite.py
• Listed in languages.json under category "Scientific"

Prerequisites

See README.md for Julia-specific installation requirements.