|
|
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.
|
|
|
Playing the Board Game Stratego by Computer
Irovský, Dominik ; Šátek, Václav (referee) ; Zbořil, František (advisor)
The topic of this thesis is the board game of Stratego. This game features incomplete information. The goal of this thesis is research of existing game playing algorithms and, design and implementation of new solution. For the new solution modified version of Monte Carlo Tree Search as well as alfa-beta algorithm and expectimax were used. The solution was implemented as a console application with possibility of future expansion. Functionality of the solution was validated and tested using experiments. Effectivity of the final algorithm was satisfying
|
|
|
Settlers of Catan
Novák, Daniel ; Hric, Jan (advisor) ; Pilát, Martin (referee)
In this thesis, we work on implementation of the board game Settlers of Ca- tan and artifitial intelligence playing this game. The artificial intelligence is based on a combination of expectimax and reinforcement learning. Using reinforcement learning, we have been able to develop an agent who can play reasonably. We ma- naged to improve the policy learned by reinforcement learning using expectimax. The resulting agent is able to win aganist average human player.
|
|
|
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.
|
guest ::
