Active Inference in Elixir

Active Inference in Elixir

Overview

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

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

• lib/active_inference.ex — Core module (normalize, KL divergence, softmax)
• lib/active_inference/agent.ex — GenServer Agent (belief updating, EFE, policy)
• lib/active_inference/application.ex — OTP Application supervisor
• demo.exs — Interactive demo script
• mix.exs — Mix project configuration
• run.sh — Execution script (handles compilation if needed)
• README.md — Usage documentation and requirements

Usage

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

Language-Specific Features

• OTP GenServer for stateful agent
• Supervision tree for fault tolerance
• Functional pipeline composition
• Pattern matching for observation dispatch

Integration

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

Prerequisites

See README.md for Elixir-specific installation requirements.