Karavolos, K.D. (2010) Using a Support Vector Machine to learn to play Othello. Bachelor's Thesis, Artificial Intelligence.
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Abstract
Like chess and backgammon, the game Othello is a popular eld of application for Machine Learning (ML) techniques. An especially e ffective technique for learning to play a game is a neural network with Temporal Dif ference Learning (TDL). Such a neural netwerk has achieved a worldclass level of play in backgammon. Another succesful, increasingly popular ML technique is the Support Vector Machine (SVM). However, this technique has not yet been applied to a game. This experiment compares the use of an SVM to learn to play Othello with the use of a TD neural network and a few other techniques for playing Othello. It appears that the player that is trained with an SVM performs better than a player with random moves. However, it is generally defeated by the heuristic positional strategy and loses almost every game versus the mobility strategy and a TD player.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:30 |
| Last Modified: | 15 Feb 2018 07:30 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/9068 |
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