Strategy extraction when playing games

Modelling of a subject's strategy is rather important task for many situations eg in economy. However, modelling is mainly done by hand, ie repeating the cycle of proposing a hypothesis model and verifying it with various statistical approaches. In our work we use machine learning tools for automatic modelling of strategies in a game which is particularly interesting from the game theoretic point of view. More precisely, we modelled the decision process in the ultimatum bargaining game, experimentally performed in 4 countries. The results were decision trees corresponding to the qualitative models of the player types, con rming intuition about the game playing and the rational behaviour hypotheses of the players.