In this thesis we present implementation and results from different algorithms and methods for playing multi-action game Less. We have used minimax algorithm, its optimization with alpha-beta pruning and Monte-Carlo tree search. All algorithms have played games between themselves and then we have analyzed results and the influence of different input parameters. Due to the huge branching factor of game Less, the Monte-Carlo tree search has proven to be better choice than minimax algorithm. In the following analysis we have discovered, that the first move advantage does not play role in the outcome of the game, while the initial setting of the tiles does. Results have shown that best designed algorithms can beat occasional player of game Less.
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