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- W2895816966 abstract "A majority of studies training agents to play mathematical games with evolution use a single population. For games with embedded conflict, like iterated prisoner's dilemma, this can yield interesting behavior, but that behavior may be partly the result of genetic collusion. This study implements a two-population agent training model in which all play is between populations, while breeding is within the populations. Results suggest that genetic collusion is at least partially responsible for the emergence of cooperation in evolutionary studies of the iterated prisoner's dilemma. This study implements a novel finite state representation called a binary decision automata that relies only on information about the agent and opponent's scores, not the moves they made, making it easy to study multiple games. The agents are applied to two games in addition to prisoner's dilemma. The first is the graduate school game, which has a beneficial strategy that is unstable and so cannot arise in a single-population training environment. The strategy is found to arise in two-population environments. The system is also tested on a simple coordination game to verify that the agent training system is functioning nominally." @default.
- W2895816966 created "2018-10-26" @default.
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- W2895816966 date "2018-07-01" @default.
- W2895816966 modified "2023-09-26" @default.
- W2895816966 title "Two Population Studies of Evolving Game Playing Agents" @default.
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- W2895816966 doi "https://doi.org/10.1109/cec.2018.8477963" @default.
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