Nine Men's Morris Good Moves Dataset

If you use these datasets as part of any work, please cite it as:

F. Chesani, A. Galassi, M. Lippi and P. Mello, "Can Deep Networks Learn to Play by the Rules? A Case Study on Nine Men's Morris," to be published in IEEE Transactions on Games, doi: 10.1109/TG.2018.2804039


Brief history of these datasets

This data sets are collections of states of the game Nine Men's Morris, which have been created with the purpose to apply machine learning to train softwares to play to the game, and to test their performances.

These datasets have been created by Andrea Galassi, as part of his master thesis in Computer Science Engineering ("Ingegneria Informatica", in italian) and as part of a successive work.
Please feel free to contact him (a.galassi *at* or his thesis supervisors (Paola Mello, paola.mello *at*, Federico Chesani federico.chesani *at* for further questions.

For further information, the thesis is available here: Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game.


Good Moves Dataset (Matches Dataset)

The dataset consist of 100,154 game states and as many good moves elaborated by an Artificial Intelligence for the game of Nine Men's Morris.

None of the states in the dataset is symmetric to any other, therefore anyone can handle the symmetries as he/she prefers.
If all the symmetric states are explored, the dataset can reach 1,628,673 pairs.

The dataset contains states both reachable and unreachable during a normal match, decreasing the probability of reaching a training state during a testing match. The moves contained in it could be different from the optimal one, however, it constitutes a good knowledge base, from which other AI system can learn to play the game.

All the data have been generated making play an Artificial Intelligence called Deep Mill against other artificial intelligence and gathering the choices made by Deep Mill during the games.

Three version of the dataset are available:


Reachable States Dataset

The dataset consist of 2,085,613 states which are reachable through a finite sequence of legal moves starting from the initial empty board configurations. It has been generated exploring the space of the game states applying random choices from a reachable configurations.

None of the states contained in this dataset is present in the Good Moves Dataset.


Data format

The dataset does not distinction between black and white checkers but only between player checkers and enemy ones. An entry of the dataset consist of a string of 31 to 35 characters: 



NNMM: Neural Nine Men's Morris

This dataset has been used to train a neural networks system called Neural Nine Men's Morris to play the game, without inserting symbolic knowledge about the game rules.
After training the system has been tested on the whole expanded dataset (therefore considering any symmetric state) and the outcome is:


For more information about neural networks, NNMM and its testing, see Neural Nine Men's Morris.


Useful free resurces