Abstracts

We explored the usage of Monte Carlo tree search (MCTS) and deep learning in order to predict game level difficulty in Candy Crush Saga (Candy) measured as number of attempts per success. A deep neural network (DNN) was trained to predict moves from game states from large amounts of game play data. The DNN played a diverse set of levels in Candy and a regression model was fitted to predict human difficulty from bot difficulty. We compared our results to an MCTS bot. Our results show that the DNN can make estimations of game level difficulty comparable to MCTS in substantially shorter time. Vi utforskade anvndning av Monte Carlo tree search (MCTS) och deep learning fr attuppskatta banors svrighetsgrad i Candy Crush Saga (Candy). Ett deep neural network(DNN) trnades fr att frutse speldrag frn spelbanor frn stora mngder speldata. DNN:en spelade en varierad mngd banor i Candy och en modell byggdes fr att frutsemnsklig svrighetsgrad frn DNN:ens svrighetsgrad. Resultatet jmfrdes medMCTS. Vra resultat indikerar att DNN:ens kan gra uppskattningar jmfrbara medMCTS men p substantiellt kortare tid.