| name | Active Inference in Racket |
| description | Racket implementation of Active Inference with belief updating, free energy minimization, and policy selection |
Active Inference in Racket
Overview
This skill provides a complete Active Inference implementation in Racket,
demonstrating Bayesian belief updating, variational free energy calculation,
and expected free energy-based policy selection.
Core Algorithms
- Belief Updating: Bayesian inference using observation likelihoods to update posterior beliefs
- Free Energy Calculation: KL divergence between posterior beliefs and prior distribution
- Policy Selection: Softmax action selection over expected free energy per action
- Perception-Action Loop: Iterative sense → infer → act cycle with generative model
Key Files
active_inference.rkt — Source implementation
run.sh — Execution script (handles compilation if needed)
README.md — Usage documentation and requirements
Usage
cd 0_CONTEXT/Computer_Languages/Racket/
./run.sh
Language-Specific Features
- Immutable data structures
- Pure function composition
- Strong type inference
Integration
- Tested via
master_controller.py test racket
- Benchmarked via
benchmark_suite.py
- Listed in
languages.json under category "Functional"
Prerequisites
See README.md for Racket-specific installation requirements.