Multi-Agent Simulation

Overview

A simulation framework where autonomous agents with minimal individual intelligence produce complex collective behaviors through local interactions. Think flocking, foraging, and cooperative task-solving — emerging from agents that only see their immediate neighbors.

Technical approach

• Agent architecture: each agent has a simple perception-action loop with a small state vector
• Environment: continuous 2D space with obstacles, resources, and optional goals
• Communication: agents can broadcast short signals to neighbors within a fixed radius
• Visualization: real-time rendering with matplotlib and optional export to video

Emergent behaviors observed

• Flocking: Reynolds-style boid rules producing natural group movement
• Foraging: pheromone-trail-based resource discovery without central coordination
• Task allocation: distributed consensus on role assignment for multi-step tasks

Connection to BrainNet

Multi-agent coordination is one of the four fundamental building blocks for BrainNet. Understanding how collective intelligence emerges from local rules is essential for designing systems where humans, AI, and robots coordinate through shared representations.