The predictability of behaviour in Connect 4
| dc.contributor.advisor | Thill, Serge | |
| dc.contributor.advisor | Selen, Luc | |
| dc.contributor.author | Dankers, Samuel | |
| dc.date.issued | 2023-01-27 | |
| dc.description.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. | |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/16024 | |
| dc.language.iso | en | |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | |
| dc.thesis.specialisation | specialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence | |
| dc.thesis.studyprogramme | studyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence | |
| dc.thesis.type | Bachelor | |
| dc.title | The predictability of behaviour in Connect 4 |
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