Machine Learning for k-in-a-row Type Games Using Random Forest and Genetic Algorithm
Name
Aleksei Loos
Abstract
The main objective of the thesis is to explore the viability of combination multiple
machine learning techniques in order to train Artificial Intelligence for k-in-a-row type games.
The techniques under observation are following:
- Random Forest
- Minimax Algorithm
- Genetic Algorithm
The main engine for training AI is Genetic Algorithm where a set of individuals are evolved
towards better playing computer intelligence. In the evaluation step, series of games are done
where individuals compete in series of games against each other – the results are recorded and
the evaluation score of the individuals are based on their performance in the games. During a
game, heuristic game tree search algorithm Minimax is used as player move advisor. Each of
the competing individuals has a Random Forest attached that is used as the heuristic function
in Minimax. The key idea of the training is to evolve as good Random Forests as possible. This
is achieved without any help of human expertise by using solely evolutionary training.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Prof. Jaak Vilo
Defence year
2012