Iterated mutual observation with genetic programming

Abstract

"This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an agent can choose from is infinite. Preliminary simulation results are presented. They show that by varying the population size of genetic programming, different learning characteristics can easily be achieved, which lead to quite different communication patterns." (author's abstract)