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Iterated Mutual Observation with Genetic Programming (2001)

by Peter Dittrich ,  Thomas Kron ,  Christian Kuck ,  Wolfgang Banzhaf
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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, di#erent learning characteristics can easily be achieved, which lead to quite di#erent communication patterns. 1

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