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Artificial Intelligence for a Board Game
Tureček, Dominik ; Baskar, Murali Karthick (referee) ; Beneš, Karel (advisor)
This work proposes and implements AI agents for the game Dice Wars. Dice Wars is turn-based, zero-sum game with non-deterministic move results. Several AI agents were created using rule-based approach, expectiminimax algorithm, and logistic regression. To evaluate the performance of proposed agents, an implementation of the game was created. Results of the experiments have shown that it's preferable to play aggressively in two-player games and make more optimal moves in games played with more players. The agent using expectiminimax is able to win more than 60 % of games in 8-player games against random players and wins 21.4 % of games played against a mix of seven other agents created in this work. In two-player setups, the agent using logistic regression with numbers of players' scores and number of dice as features has the best performance and wins 59.4 % of games in average.
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Artificial intelligence for the Game Carcassonne: The Discovery
Motlíček, Ondřej ; Hric, Jan (advisor) ; Zelinka, Mikuláš (referee)
The bachelor paper deals with the development of an artificial intelligence for the game Carcassonne - The Discovery. Different approaches for designing an artificial intel- ligence are presented. Heuristic functions based on various aspects of the game. Monte Carlo methods and the Expectiminimax algorithm are used for state space of the game. The designed methods are implemented and experimentally tested and compared by simulations of the game between the artificial players. Results of the experiment are pre- sented and explained. The simulation environment consists of multiple programs for the game simulation of both artificial and human players. A batch simulation of the artificial intelligence is emphasized. 1
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Artificial Intelligence for a Board Game
Tureček, Dominik ; Baskar, Murali Karthick (referee) ; Beneš, Karel (advisor)
This work proposes and implements AI agents for the game Dice Wars. Dice Wars is turn-based, zero-sum game with non-deterministic move results. Several AI agents were created using rule-based approach, expectiminimax algorithm, and logistic regression. To evaluate the performance of proposed agents, an implementation of the game was created. Results of the experiments have shown that it's preferable to play aggressively in two-player games and make more optimal moves in games played with more players. The agent using expectiminimax is able to win more than 60 % of games in 8-player games against random players and wins 21.4 % of games played against a mix of seven other agents created in this work. In two-player setups, the agent using logistic regression with numbers of players' scores and number of dice as features has the best performance and wins 59.4 % of games in average.
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