  In imperfect information games, the game state is generally not fully
observable to players. Therefore, good gameplay requires policies that deal
with the different information that is hidden from each player. To combat this,
effective algorithms often reason about information sets; the sets of all
possible game states that are consistent with a player's observations. While
there is no way to distinguish between the states within an information set,
this property does not imply that all states are equally likely to occur in
play. We extend previous research on assigning weights to the states in an
information set in order to facilitate better gameplay in the imperfect
information game of Reconnaissance Blind Chess. For this, we train two
different neural networks which estimate the likelihood of each state in an
information set from historical game data. Experimentally, we find that a
Siamese neural network is able to achieve higher accuracy and is more efficient
than a classical convolutional neural network for the given domain. Finally, we
evaluate an RBC-playing agent that is based on the generated weightings and
compare different parameter settings that influence how strongly it should rely
on them. The resulting best player is ranked 5th on the public leaderboard.
