cognitive/docs/research/ant_colony_active_inference.md

title	type	status	created	tags	semantic_relations
Active Inference in Ant Colony Behavior	research	stable	2024-02-07	active_inference ant_colony collective_behavior	type links implements research_document_template type links relates knowledge_base/cognitive/collective_behavior knowledge_base/cognitive/active_inference

Active Inference in Ant Colony Behavior

Overview

Research Question

How can active inference principles explain and model emergent collective behavior in ant colonies, particularly in foraging and path optimization?

Significance

Understanding how simple agents using active inference principles can produce complex collective behaviors has implications for both biological systems and artificial swarm intelligence.

Related Work

• knowledge_base/cognitive/collective_behavior_ants
• knowledge_base/cognitive/stigmergic_coordination
• knowledge_base/cognitive/swarm_intelligence

Theoretical Framework

Core Concepts

• knowledge_base/cognitive/active_inference
• knowledge_base/cognitive/free_energy_principle
• knowledge_base/cognitive/emergence_self_organization

Mathematical Foundation

def compute_expected_free_energy(beliefs, policies):
    """
    Compute expected free energy for policy evaluation.
    
    Args:
        beliefs: Current belief state about environment
        policies: Available action policies
        
    Returns:
        Expected free energy for each policy
    """
    pragmatic_value = compute_pragmatic_value(beliefs, policies)
    epistemic_value = compute_epistemic_value(beliefs, policies)
    
    return pragmatic_value + epistemic_value

Methodology

Experimental Design

1. Implementation of individual ant agents using active inference
2. Environment design with pheromone trails and food sources
3. Collective behavior emergence through local interactions
4. Analysis of path optimization and foraging efficiency

Implementation

class Nestmate:
    """
    Individual ant agent using active inference.
    
    Attributes:
        position: Current position in environment
        beliefs: Beliefs about environment state
        policies: Available action policies
    """
    
    def __init__(self, config):
        """Initialize agent with configuration."""
        self.position = Position(x, y, theta)
        self.beliefs = initialize_beliefs()
        self.policies = generate_policies()
        
    def update(self, dt, world_state):
        """Update agent state and take action."""
        # Update beliefs based on observations
        observation = self.observe(world_state)
        self.update_beliefs(observation)
        
        # Select and execute action
        action = self.select_action()
        self.execute_action(action, dt)
        
    def update_beliefs(self, observation):
        """Update beliefs using variational inference."""
        pass
        
    def select_action(self):
        """Select action using expected free energy."""
        G = compute_expected_free_energy(self.beliefs, self.policies)
        return select_policy(G)

Validation Methods

• Path efficiency metrics
• Food collection rate
• Emergence of optimal trails
• Collective behavior analysis

Results

Data Analysis

def analyze_colony_behavior(simulation_data):
    """
    Analyze collective behavior patterns.
    
    Args:
        simulation_data: Recorded simulation data
        
    Returns:
        Analysis metrics and visualizations
    """
    # Compute path optimization metrics
    path_efficiency = compute_path_efficiency(simulation_data)
    
    # Analyze pheromone trail formation
    trail_formation = analyze_trail_formation(simulation_data)
    
    # Measure food collection efficiency
    foraging_efficiency = compute_foraging_efficiency(simulation_data)
    
    return {
        'path_efficiency': path_efficiency,
        'trail_formation': trail_formation,
        'foraging_efficiency': foraging_efficiency
    }

Key Findings

1. Active inference enables efficient path optimization
2. Emergent trail patterns match biological observations
3. Collective behavior emerges from individual inference

Visualizations

def visualize_results(results):
    """Create visualizations of colony behavior."""
    plt.figure(figsize=(12, 8))
    
    # Plot pheromone trails
    plt.subplot(221)
    plot_pheromone_trails(results)
    
    # Plot path efficiency
    plt.subplot(222)
    plot_path_efficiency(results)
    
    # Plot food collection
    plt.subplot(223)
    plot_food_collection(results)
    
    plt.tight_layout()
    plt.show()

Discussion

Interpretation

• Active inference provides a principled explanation for ant behavior
• Local inference leads to global optimization
• Pheromone trails serve as external memory

Implications

1. New insights into biological systems
2. Improved swarm robotics algorithms
3. Applications to distributed optimization

Limitations

• Computational complexity scaling
• Simplified environment model
• Limited agent capabilities

Implementation Details

Environment Setup

# Setup virtual environment
python -m venv env
source env/bin/activate

# Install dependencies
pip install -r requirements.txt

Dependencies

import numpy as np
import torch
import matplotlib.pyplot as plt
from dataclasses import dataclass
from typing import List, Dict

Configuration

simulation:
  max_steps: 10000
  timestep: 0.1
  random_seed: 42

environment:
  size: [100, 100]
  food_sources: 5
  obstacles: 10

agent:
  sensor_range: 5
  movement_speed: 1
  rotation_speed: 0.5

Reproducibility

Code Repository

• Repository: cognitive/Things/Ant_Colony
• Main simulation: simulation.py
• Agent implementation: agents/nestmate.py

Data

• Simulation recordings
• Analysis results
• Visualization data

Environment

• Python 3.8+
• NumPy, PyTorch
• Matplotlib for visualization

Extensions

Future Work

1. Hierarchical active inference models
2. Multi-colony interactions
3. Dynamic environment adaptation

Open Questions

• Optimal balance of exploration/exploitation
• Scaling to larger colonies
• Transfer to robotic systems

References

Academic References

1. Friston, K. "The free-energy principle: a unified brain theory?"
2. Deneubourg, J.L. "The Self-Organizing Exploratory Pattern of the Argentine Ant"
3. Ramstead, M.J.D. "A tale of two densities: active inference is enactive inference"

Code References

• Things/Ant_Colony/simulation.py
• Things/Ant_Colony/agents/nestmate.py
• Things/Ant_Colony/visualization/renderer.py

Documentation

• docs/guides/ant_colony_guide
• docs/api/ant_colony_api
• docs/examples/ant_colony_example

Related Research

Prerequisites

• research/active_inference_foundations
• research/collective_behavior_basics

Follow-up Work

• research/hierarchical_swarms
• research/multi_colony_systems