Learning to Communicate: The Emergence of Signaling in Spatialized Arrays of Neural Nets

We work with a large spatialized array of individuals in an environment of drifting food sources and predators. The behavior of each individual is generated by its simple neural net; individuals arecapable of making one of two sounds and are capable of responding to sounds from their immediate neighbors by opening their mouths or hiding. An individual whose mouth is open in the presence of food is 'fed' and gains points; an individual who fails to hide when a predator is present is 'hurt' by losing points. Opening mouths, hiding, and making sounds each exact an energy cost. There is no direct evolutionary gain for acts of cooperation or 'successful communication' per se.

In such an environment we start with a spatialized array of neural nets with randomized weights. Using standard learning algorithms, our individuals 'train up' on the behavior of successful neighbors at regular intervals. Given that simple setup, will a community of neural nets evolve a simple language for signaling the presence of food and predators? With important qualifications, the answer is yes.'In a simple spatial environment, pursuing individualistic gains and using partial training on successful neighbors, randomized neural nets can learn to communicate.