A Comparison of Population Learning and Cultural Learning in Artificial Life Societies

摘要

This paper examines the effect of the addition of cultural learning to a population of agents. Experiments are undertaken using an artificial life simulator capable of simulating population learning (through genetic algorithms) and lifetime learning (through the use of neural networks). To simulate cultural learning, the exchange of information through non-genetic means, a group of highly fit agents is selected at each generation to function as teachers which are assigned a number of pupils to instruct. Cultural exchanges occur through a hidden layer of an agent's neural network known as the verbal layer. Through the use of back-propagation, a pupil agent imitates the teacher's behaviour and overall population fitness is increased. We show that the addition of cultural learning is of great benefit to the population and that in addition, cultural learning causes the population to converge on a fixed lexicon describing its environment.