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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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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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Intelligent Reactive Agent for the Game Ms.Pacman
Bložoňová, Barbora ; Zbořil, František (referee) ; Drahanský, Martin (advisor)
This thesis focuses on artificial intelligence for difficult decision problemes such as the game with uncertainty Ms. Pacman. The aim of this work is to design and implement intelligent reactive agent using a method from the field of reinforcement learning, demonstrate it on visual demo Ms.Pacman and compare its intelligence with well-known informed methods of playing games (Minimax, AlfaBeta Pruning, Expectimax). The thesis is primarily structured into two parts. The theoretical part deals with adversarial search (in games), reactivity of agent and possibilities of machine learning, all in the context of Ms. Pacman. The second part addresses the design of agent's versions behaviour implementation and finally its comparison to other methods of adversarial search problem, evaluation of results and a few ideas for future improvements.
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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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