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Game Playing with Uncertainty
Bajza, Jakub ; Zbořil, František (referee) ; Zbořil, František (advisor)
This Bachelor thesis describes the implementation of expectiminimax algorithm for zero-sum games. It also introduces the complications, that you can face, if working on applying the expectiminimax algorithm to more complicated games of this category. This thesis also presents a way to create an evaluation function for computer opponent. The applicability of these evaluation functions is demonstrated by series of tests, where human player plays against computer opponent or two computer opponents play against each other.
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Strategic Game with Uncertainity
Tulušák, Adrián ; Šimek, Václav (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous functional system for the game Scotland Yard by using artificial intelligence methods for game theory and machine learning. The problem is solved by algorithm of game theory - Alpha Beta. There was an attempt to use machine learning, but it proved to be unsuccessful due to the large number of states for expansion and insufficient computational recourses. The solution using Alpha Beta algorithm was tested on human players and it proved the ability of artificial intelligence to fully compete against real players. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. Based on these experiments, the thesis also introduces some improvements that could utilize machine learning and extend the existing solution.
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Strategic Game with Uncertainity
Tulušák, Adrián ; Šimek, Václav (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous functional system for the game Scotland Yard by using artificial intelligence methods for game theory and machine learning. The problem is solved by algorithm of game theory - Alpha Beta. There was an attempt to use machine learning, but it proved to be unsuccessful due to the large number of states for expansion and insufficient computational recourses. The solution using Alpha Beta algorithm was tested on human players and it proved the ability of artificial intelligence to fully compete against real players. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. Based on these experiments, the thesis also introduces some improvements that could utilize machine learning and extend the existing solution.
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|
|
Game Playing with Uncertainty
Bajza, Jakub ; Zbořil, František (referee) ; Zbořil, František (advisor)
This Bachelor thesis describes the implementation of expectiminimax algorithm for zero-sum games. It also introduces the complications, that you can face, if working on applying the expectiminimax algorithm to more complicated games of this category. This thesis also presents a way to create an evaluation function for computer opponent. The applicability of these evaluation functions is demonstrated by series of tests, where human player plays against computer opponent or two computer opponents play against each other.
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