The predictability of behaviour in Connect 4
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2023-01-27
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en
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Abstract
The ability to predict behaviour can boost the development and safe implementation
of A.I.. However, the ability to predict behaviour is difficult
task to accomplish. Therefore, the game Connect 4 was taken as a simplified
environment in which predictions were made about what move players
would play. A computer system was created that was able to play Connect
4 on different levels using the Monte Carlo Tree Search algorithm. Neural
networks were provided with the task to predict which move the computer
would play given the current game-state after the networks were trained
on already collected training data. The experiment included a part where
additional data was provided to the networks and the accuracies of the predictions
were measured to monitor how proficient the networks would be at
adapting to players from a different level. The initial accuracies could reach
50% depending on the network and play level. By providing data of games
played on a different level than the collected training data it was possible
to monitor the changes of accuracies of networks. The biggest change in
accuracy measured was an improvement of 31,6% compared to the initial
accuracy with solely the original training data.
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Faculteit der Sociale Wetenschappen