Increasing Population Diversity Through Cultural Learning.

A number of learning models are commonly employed in the simulation of social behavior. These include population learning, lifetime learning and cultural learning. Population learning allows populations as a whole to evolve over time, typically through a Darwinian model of natural selection. Lifetime learning allows individuals to acquire knowledge during their lifetimes and cultural learning allows individuals to pass this knowledge to their peers or subsequent generations. This work examines the effects of cultural learning on both the fitness and the diversity of a population of neural network agents. A population employing population learning alone and one employing both population and cultural learning are assigned three benchmark tasks: the 5-bit parity problem, the game of tic-tac-toe and the game of connect-four. Each agent contains a genome which encodes a neural network controller used by the agent to perceive and react to environmental stimuli. Results show that the addition of cultural learning promotes improved fitness and significantly increases both genotypic (the genetic make up of individuals) and phenotypic (the behavior of individuals) diversity in the population. [ABSTRACT FROM AUTHOR]