Project Abstract

As we progress, we develop technologies that assist us from the most miniscule tasks to the most complex of tasks. One prominent technology that was invented that will be the future is artificial intelligence (AI). Currently, we can see AI in almost every aspect of human life, from music recommendations to text generation to help with work. Thus, studying the basis of AI became more and more essential. And so, in this project, we take a look at a simple AI algorithm, alpha-beta pruning. Alpha-beta pruning is an optimisation technique on a na�?ve minimax algorithm. Alpha-beta pruning would drastically reduce the time that the AI need to search for an optimal move to win a game, and so it could reach double the depth for more optimal moves in the same amount of time. In this project, we test this theory on a two-player turn based game called �� ��n Quan. We will be implementing this in Python, using Pygame for GUI features. While alpha-beta met the expectations for faster search compared to minimax, it does not meet the expectations for double depths in the same amount of time. This could be due to the fact that this game produces up to 10 different states each depth and made it exponentially big. Beside that, the algorithm generates states very na�?vely.

Keywords: Artificial Intelligence, Game Theory, Visual Computing

 Conference Details

Session: Poster Session B at Poster Stand 90

Location: Sir Stanley Clarke Auditorium at Wednesday 8th 09:00 – 12:30

Markers: Ulrich Berger, Nicholas Micallef

Course: BSc Computer Science, 3rd Year

Future Plans: I’m looking for work