Nine Men's Morris is a game of great strategic complexity. The ancient board game of Nine Men's Morris, also known as the Mills game, Merelles, Mühle spiel, Malom or Cowboy Checkers, has been played for over 2,000 years. The system has an accuracy of the whole move of about 37%įor more information about neural networks, NNMM and its testing, see Neural Nine Men's Morris.Nine Men's Morris Book of Board Game Strategy.The system has learnt the rules (99% of cases the chosen move is legal).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.Īfter training the system has been tested on the whole expanded dataset (therefore considering any symmetric state) and the outcome is: An hyphen divides the game state from the move description, which is written as pairs of coordinates letter-number the meaning of each coordinate depends on the game phase: the parts of the move are written (if present) in the order FROM, TO and REMOVE.A sequence of 4 numbers completes the state representation, where the first two numbers represent, respectively, the number of checkers that the player has in its hands and the ones that his adversary has the last two represent, in the same order, the number of checker that the players have on the board.The position are represented in order as they appear from left to right and from top to bottom The first 24 characters describe the board state with a letter representing the state of each position: O if the position is empty, M (Mine) if there is a checker of the player and E (Enemy) if there is an opponent one.An entry of the dataset consist of a string of 31 to 35 characters: The dataset does not distinction between black and white checkers but only between player checkers and enemy ones. The EXTENDED STATES DATASET contains all the states.The COMPLETE STATES DATASET contains all the states, without the symmetric ones.None of the states contained in this dataset is present in the Good Moves Dataset. It has been generated exploring the space of the game states applying random choices from a reachable configurations. ![]() 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. The EXPANDED DATASET contains all the data and the symmetric pairs.Therefore it contains states which are more unlikely to be reached during a match. The GAMING DATASET do not contains data coming from a regular match starting with an empty board.The COMPLETE DATASET contains all the data, without the symmetric pairs.Three version of the dataset are available: 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.Īll 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. The dataset contains states both reachable and unreachable during a normal match, decreasing the probability of reaching a training state during a testing match. If all the symmetric states are explored, the dataset can reach 1,628,673 pairs. None of the states in the dataset is symmetric to any other, therefore anyone can handle the symmetries as he/she prefers. ![]() 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. Please feel free to contact him (a.galassi *at* ) or his thesis supervisors (Paola Mello, llo *at* , Federico Chesani federico.chesani *at* ) for further questions.įor 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. 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.
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