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cognitive/docs/guides/learning_paths/swarm_intelligence.md
Daniel Ari Friedman a61f13a26f Updates
2025-02-07 11:08:25 -08:00

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title type status created tags semantic_relations
Swarm Intelligence Learning Path learning_path stable 2024-02-07
swarm_intelligence
collective_behavior
learning
type links
implements
learning_path_template
type links
relates
knowledge_base/cognitive/swarm_intelligence
knowledge_base/cognitive/collective_behavior

Swarm Intelligence Learning Path

Overview

This learning path guides you through understanding and implementing swarm intelligence systems, with special focus on biologically-inspired collective behavior. You'll learn theoretical principles, mathematical models, and practical implementations using the ant colony example.

Prerequisites

Required Knowledge

Recommended Background

  • Python programming
  • Basic agent-based modeling
  • Complex systems concepts

Learning Progression

1. Foundations (Week 1-2)

Core Concepts

Practical Exercises

Learning Objectives

  • Understand swarm principles
  • Implement basic swarm behaviors
  • Analyze emergent patterns

2. Ant Colony Systems (Week 3-4)

Advanced Concepts

Implementation Practice

Learning Objectives

  • Implement pheromone systems
  • Model foraging behavior
  • Develop path optimization

3. Advanced Implementation (Week 5-6)

Core Components

Projects

Learning Objectives

  • Implement complete colony system
  • Integrate active inference
  • Develop advanced features

Implementation Examples

Basic Swarm Agent

class SwarmAgent:
    def __init__(self, config):
        self.position = initialize_position()
        self.velocity = initialize_velocity()
        self.sensors = create_sensors()
        
    def update(self, neighbors, environment):
        """Update agent state based on local information."""
        # Process sensor information
        local_info = self.sensors.process(neighbors, environment)
        
        # Update movement
        self.velocity = compute_velocity(local_info)
        self.position += self.velocity
        
    def interact(self, environment):
        """Interact with environment (e.g., deposit pheromones)."""
        pass

Ant Colony Implementation

class AntColony:
    def __init__(self, config):
        self.agents = create_agents(config)
        self.environment = create_environment(config)
        self.pheromone_grid = initialize_pheromones()
        
    def update(self, dt):
        """Update colony state."""
        # Update agents
        for agent in self.agents:
            observation = self.environment.get_local_state(agent.position)
            agent.update(dt, observation)
            
        # Update environment
        self.environment.update(dt)
        self.pheromone_grid *= self.config.pheromone_decay
        
    def run_simulation(self, steps):
        """Run simulation for specified steps."""
        for step in range(steps):
            self.update(self.config.timestep)
            self.collect_data(step)

Study Resources

Core Reading

Code Examples

Additional Resources

  • Research papers
  • Video tutorials
  • Interactive simulations

Assessment

Knowledge Checkpoints

  1. Swarm fundamentals
  2. Ant colony systems
  3. Advanced implementations
  4. Real-world applications

Projects

  1. Mini-project: Basic swarm simulation
  2. Implementation: Ant colony system
  3. Final project: Advanced application

Success Criteria

  • Working swarm implementation
  • Ant colony simulation
  • Advanced features
  • Performance optimization

Next Steps

Advanced Paths

Specializations

Related Paths

Prerequisites

Follow-up Paths

Common Challenges

Theoretical Challenges

  • Understanding emergence
  • Modeling collective behavior
  • Analyzing system dynamics

Implementation Challenges

  • Efficient simulation
  • Scalability issues
  • Visualization complexity

Solutions

  • Start with simple models
  • Incremental complexity
  • Regular validation
  • Performance profiling

Example Configurations

Basic Swarm Config

swarm:
  population_size: 100
  sensor_range: 5.0
  max_speed: 2.0
  interaction_radius: 3.0

environment:
  size: [100, 100]
  obstacles: 10
  boundary_conditions: "periodic"

Ant Colony Config

colony:
  ants: 50
  nest_location: [50, 50]
  pheromone_decay: 0.99
  
foraging:
  food_sources: 5
  food_value: 1.0
  max_steps: 10000

Visualization Tools

Basic Visualization

def visualize_swarm(agents, environment):
    """Visualize swarm behavior."""
    plt.figure(figsize=(10, 10))
    
    # Plot agents
    positions = [agent.position for agent in agents]
    plt.scatter([p.x for p in positions], 
                [p.y for p in positions])
    
    # Plot environment
    environment.plot()
    plt.show()

Advanced Analysis

def analyze_behavior(simulation_data):
    """Analyze collective behavior patterns."""
    # Compute metrics
    coherence = compute_coherence(simulation_data)
    efficiency = compute_efficiency(simulation_data)
    
    # Visualize results
    plot_metrics(coherence, efficiency)