| Abstract: |  The goal of General Game Playing (GGP) is to create intelligent agents that are able to play any game based only on the description of the games rules. In this thesis we focus on GGP for games with imperfect information. Compared with perfect information games the imperfect information games bring with them numerous new challenges that must be tackled, for example: Player must find a way how to work with the uncertainty about her current state of the world, or cope with randomness in the game.
The main outcome of this thesis is TIIGR, one of the first GGP agents for games with imperfect information. Our agent uses a simulation-based approach to action selection. Specifically it uses perfect information sampling Monte Carlo with Upper Confidence Bound applied to Trees as its main reasoning system.
Further we explain the Game Description Language-II (GDL-II) and on a game we created we explain how to create games in this language.
The ability of our player to play conceptually different games and her performance was verified on several games including Latent Tic-Tac-Toe, Liar’s dice and cards.
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