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Agents to Play Connect – Four Using Cultural Learning

This paper describes a cultural learning approach to the evolution of agents to play the game of connect–four. Each agent has a neural network responsible for perceiving the current board configuration and selecting an appropriate next move. Populations evolve through population learning, a process of Darwinian evolution, using genetic algorithms. Cultural learning is implemented by selecting highly fit agents as teachers to instruct the next generation. Teachers communicate with pupils through a hidden layer in each neural network (the verbal input/output layer) and pupils attempt to replicate utterances by back–propagation. Experiments are conducted comparing the performance of populations employing population learning alone and populations employing both population and cultural learning.