Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess
Sprache des Vortragstitels:
Deutsch
Original Tagungtitel:
IEEE Conference on Games
Sprache des Tagungstitel:
Englisch
Original Kurzfassung:
Research in Game Artificial Intelligence distinguishes between fully observable, perfect-information games and
imperfect-information games, which hide part of the game?s full
information. In games with imperfect information, all possible
game states that are consistent with a player?s currently available
information about the progress of the game are called the information set for that player. This information set can be used
for multiple purposes such as determining the expected outcome
of a certain move by evaluating it on all possible states in the
information set. While in theory there is no way to distinguish
states within an information set, players can use experience
and other context information to estimate which states are the
most likely. In this paper, we estimate a probability distribution
over an information set from historic data such that we can
assign a weight to each individual state. We achieve this by
training a Siamese neural network with triplets of comparisons
between different states in the information set given the context
of the previously obtained information. A first evaluation in the game of Reconnaissance Blind Chess shows that we can learn
to identify the one true game state in a large information set
with high probability. In addition, when used within a naively
constructed RBC agent, this approach shows promising gameplay
performance. At the time of writing, a simple agent based on the Siamese neural network is ranked #6 of all agents on the public RBC leaderboard.
Sprache der Kurzfassung:
Englisch
Englischer Vortragstitel:
Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess
Englischer Tagungstitel:
IEEE Conference on Games
Englische Kurzfassung:
Research in Game Artificial Intelligence distinguishes between fully observable, perfect-information games and
imperfect-information games, which hide part of the game?s full
information. In games with imperfect information, all possible
game states that are consistent with a player?s currently available
information about the progress of the game are called the information set for that player. This information set can be used
for multiple purposes such as determining the expected outcome
of a certain move by evaluating it on all possible states in the
information set. While in theory there is no way to distinguish
states within an information set, players can use experience
and other context information to estimate which states are the
most likely. In this paper, we estimate a probability distribution
over an information set from historic data such that we can
assign a weight to each individual state. We achieve this by
training a Siamese neural network with triplets of comparisons
between different states in the information set given the context
of the previously obtained information. A first evaluation in the game of Reconnaissance Blind Chess shows that we can learn
to identify the one true game state in a large information set
with high probability. In addition, when used within a naively
constructed RBC agent, this approach shows promising gameplay
performance. At the time of writing, a simple agent based on the Siamese neural network is ranked #6 of all agents on the public RBC leaderboard.
Forschungs-