Artikel,
Iterated Mutual Observation with Genetic Programming
Sozionik Aktuell, (Juli 2001)
Zusammenfassung
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.
