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W09. Emergence 04

Today, we introduce simple swarms, which are multi-agent systems. The difference between a swarm and cellular automata is that a swarm is comprised of agents that act over a surface or environment, whereas the cellular automata emerges out of the environment itself. The cellular automata are at the "atom" level, where we can see the emergence of higher-level structures like cell walls. The swarms are at the "cell" level, where we can see the emergence of cell bodies, specialized parts, and "multi-cellular" organisms. Again, although nothing is "intelligent" in any of the agents in these swarms, when allowed to interact, complex structures start to emerge, and something like "decisions" seem to start to happen.

Pre-readings and Videos​

The simulations we will see today illustrate different types of simple swarms. Here are the papers, videos, and websites that explain the phenomena in detail.

Physarum​

This video explains a kind of slime mold that exhibits intelligent behaviour without having a brain or a central nervous system.

Along with the video, there are many interesting readings for physarum. Sage Jenson's Physarum artistic exploration into physarum gives a fascinating and beautiful demonstration of emergence, as well as a good technical explanation of how to model physarum. Importantly, even this excellent computer model is nowhere near powerful enough to accurately model a real slime mold.

Physarum builds off of our understanding of Langton's Ant and Conway's Game of Life, where the environment and agent is not a clear distinction. This is a demonstration of the externalization of cognition.

Traffic Waves​

A very simple (nearly) 1-dimensional model of emergence in a multi-agent system using a real-life phenomenon known as a "traffic snake," where drivers on a highway will slow down at a particular point even though there is no "good reason" to do so.

Here is a good, simple website explaining the phenomenon: trafficwaves.org.

Here is a good, simple website simulating the phenomenon: traffic-simulation.de

Flocking​

If we allow our agents to have longer-range sensing, group behaviours emerge that seem to express collective intelligence. Boids are a good example of simulating flocking behaviours in birds, schools of fish, or other animals that form large groups. In our class terms, this would be like having a swarm of PID robots trying to maintain distance on all sides. These physics-based simulators again do not exhibit actual intelligence at the individual level, yet very much look like they do at a group level.

Summary of the Day​

Learning Goals​

  1. Explain the differences between cellular automata and swarms.
  2. Understand and create simple swarm agents.
  3. Design experiments that can characterize swarm phase transitions.