Early on, I experimented with emergent behaviors using a simple evolutionary neural network algorithm. While it sounds complex, the core idea was straightforward - simulate natural evolution to automatically develop behaviors.

The neural networks had inputs connected to sensors, hidden layers of "neurons" with adjustable weights, and outputs tied to actions. The network structure was randomly initialized.

These networks controlled basic organisms in a software environment. The organisms competed and the least fit would "die off" each round.
A key step was randomly mutating the neural networks by tweaking the number of neurons, changing connection weights, or altering the network structure.

Over many generations of selection pressure, I observed the organisms develop surprisingly effective and complex behaviors entirely on their own, without any direct programming.

This demonstrated how the basic principles of evolution and neuroscience can produce robust intelligence through iteration on simple rules. The emergent results exceeded hand coded logic. While rudimentary, it provided a promising starting point for developing lifelike robot behaviors.

You can run the program here.