Machine Learning for k-in-a-row Type Games Using Random Forest and Genetic Algorithm

Antud töö põhieesmärgiks oli uurida kui efektiivne ja mõistlik on kombineerida mitu erinevat masinõppe meetodit, et treenida tehisintellekti k-ritta tüüpi mängudele. Need meetodid on järgnevad: geneetiline algoritm, juhumetsad (koos otsustuspuudega) ning Minimax algoritm. Eriliseks teeb sellise meetodi asjaolu, et kogu intelligents treenitakse ilma inimese ekspert teadmisteta ning kõik vajaliku informatsiooni peab arvuti ise endale omandama.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