https://github.com/activeinferenceinstitute/biofirm

https://github.com/activeinferenceinstitute/biofirm

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Repository

Basic Info
  • Host: GitHub
  • Owner: ActiveInferenceInstitute
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 378 MB
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

Biofirm

A framework for ecological system control using Active Inference agents.

Overview

Biofirm consists of two main components:

  1. Ecosystem Control System

    • Active Inference-based multi-agent control framework
    • Homeostatic regulation of ecological parameters
    • Comparative analysis between random and controlled dynamics
  2. BioPerplexity Analysis

    • California county-level bioregion research using Perplexity.ai API
    • Business case generation and pitch development
    • Cross-document visualization and analysis

System Architecture

Active Inference Framework

  • Multi-agent POMDP (Partially Observable Markov Decision Process) implementation
  • Each agent controls one ecological modality through free energy minimization
  • Collective homeostatic regulation through distributed control

Key Components: - Ecosystem_Simulation.py: Main simulator comparing random vs. active inference control - Biofirm_Agent.py: PyMDP-based active inference agent implementation - POMDP_ABCD.py: Generative model matrix generation (A,B,C,D matrices) - utils/: Ecological configuration files and parameters

Analysis Tools

  • Free_Energy_Minimization.py: Analysis of control performance
  • Noise_Control_ActiveInference_Sweep.py: Parameter sweep across noise and control levels

BioPerplexity Pipeline

  1. 1_Research_Bioregions.py: County-level data collection
  2. 2_Biofirm_Business_Pitch.py: Business case generation
  3. 3_Biofirm_Visualization.py: Data visualization

Getting Started

  1. Environment Setup: bash python Startup.py # Creates PyMDP virtual environment source venv/bin/activate

  2. Configuration:

  3. Adjust ecological parameters in utils/ecosystem_config.json

  4. Configure agent parameters in generative model files

  5. Run Simulations: bash python Scripts/Ecosystem_Simulation.py

Technical Details

See Biofirm_Generative_Model.md for comprehensive documentation of: - POMDP framework implementation - Generative model architecture - Free energy minimization approach - Control system dynamics

Project Structure

Bio_Perplexity/ # Business analysis tools Scripts/ # Core simulation code utils/ # Configuration files Outputs/ # Simulation results Stream/ # Documentation

Owner

  • Name: Active Inference Institute
  • Login: ActiveInferenceInstitute
  • Kind: user
  • Location: Online
  • Company: Active Inference Institute

http://activeinference.org/

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