Development Of A Five-In-A-Row Computer Agent For Measuring Cognitive State

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Supervisor:
Dr. Pataki Béla
Department of Measurement and Information Systems

The goal of this work was to help the diagnosis of MCI with games. From many possibilities I chose five-in-a-row (gomoku). Because of the game’s complexity I developed agents which are capable to evaluate the efficiency of a move in a possible situation. This allows me to use the agents as opponents for a human player or to use it to evaluate a game as an observer.

The non-learning agent is entirely algorithmic. I used it later to teach other agents. My goal was not to create a perfect player but rather a strong enough opponent.

One learning algorithm used reinforced learning with pattern recognition. I researched different reinforcing methods like self-learning and learning by observing two skilled players. The agent contains its own database equipment, and data-compression method. I tested its capacity, performance, storage ability and the limits of the agent.

The other learning agent is a neural network. For the network I chose MLP. During the teaching I observed the different effects of changing the structure of the net and the input database.

The agents were tested on a control group and I compared the results with the players’ actual performance.

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