Dynamics Difficulty Adjusment Metode Evolutionary MCTS with Flexible Search Horizon pada Multi-Action Adversarial Games untuk Penyesuaian Tingkat Permainan - Andhika Evantia Irawan; Liliana Liliana; Hans Juwiantho (Jurnal Infra, 2021)
Dynamic Difficulty Adjustment (DDA) is a method that modifies AI behavior to suit the player's abilities. So far, research on DDA in Monte Carlo Tree Search has been able to provide an appropriate level of challenge. However, the advantages of MCTS in finding solutions to long-term strategies have not been maximally implemented because so far it is only used in 2D real-time fighting games, which are short-term strategy game.This study combines DDA with evolutionary monte carlo tree search with flexible horizon (FH-EMCTS). FH-EMCTS is combination of vanilla MCTS with an Evolutionary algorithm. This method increases the length of the search space to certain extent. Giving DDA to FH-EMCTS is done by changing the way of selecting actions and assessing each node.The result of this research is that AI agents that use FH-EMCTS with DDA can be implemented into multi-action adversarial game and can provide balanced level of difficulty to other AI agents and humans. Based on the results of survey of AI agents against humans, it shows that the most fun and realistic AI agents are not the AI agents who have the best ability of winning percentage but AI agents who have win rate of around 50%.