![](bg7.png)
the effective playstyles may change dynamically de-
pending on the playstyle of the majority of the players
in each generation.
6 CONCLUSIONS AND FUTURE
WORK
In this study, we proposed a framework using ge-
netic algorithms and clustering to generate multiple
playstyles for Geister, a two-player imperfect infor-
mation game. Specifically, many agents with genes
are generated as the parameters of a function to guess
the color of the opponent’s piece. While these are
played against each other, agents with high fitness are
obtained. Furthermore, by clustering the genes pos-
sessed by the elites of all generations, we can obtain
our target multiple playstyles. As a result of the exper-
iment, we observed that five playstyles with circular
dominance relationships were generated.
In this experiment, we have considered playstyles
that focused only on the guessing manner; however,
in the future, we would like to generate more di-
verse playstyles determined by the border evaluation
functions. In addition, based on methods such as re-
inforcement learning, we investigated an improved
framework that allowed agents to play better with
characteristic playstyles.
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