Crushing Candy Crush : Predicting Human Success Rate in a Mobile Game using Monte-Carlo Tree Search

Abstract: The purpose of this thesis is to evaluate the possibility of predicting difficulty, measured in average human success rate (AHSR), across game levels of a mobile game using a general AI algorithm. We implemented and tested a simulation based bot using MCTS for Candy. Our results indicate that AHSR can be predicted accurately using MCTS, which in turn suggests that our bot could be used to streamline game level development. Our work is relevant to the field of AI, especially the subfields of MCTS and single-player stochastic games as Candy, with its diverse set of features, proved an excellent new challenge for testing the general capabilities of MCTS. The results will also be valuable to companies interested in using AI for automatic testing of software.