Adjustment of Difficulty Level on Wobble Board-Based Game Using Monte Carlo Tree Search Algorithm

Abstract

There are many training methods applied using video game as a medium to improve user motivation in training. Besides its game design, the setting of difficulty level also affects user motivation. If a game is too difficult, its player will be stressful. And if it's too easy, its player will be bored quickly. A game must balance player's skill and challenge provided in it. Dynamic Difficulty Adjustment (DDA) is a technique used to adjust difficulty level in a game with its player's skill, using Artificial Intelligence (AI) or Algorithm. Monte Carlo Tree Search can be applied by using AI DDA agent to convert option policy and playout evaluation heuristically. It is applied to balance the difficulty level with player's skill. A test has been carried out by testing AI DDA agent's accuracy and comparing the effects of every difficulty level strategy in a balance training game with wooble board-based. Its result shows that AI DDA agent is able to adjust difficulty level with 82% accuracy. However, the strategy comparison of difficulty level has no significant difference, but one of the parameters, i.e. Health Point, shows that the game can adjust difficulty level with player's skill.