Peningkatan Kemenangan Non-Playable Character dalam Permainan Triple Triad Menggunakan Alpha-Beta Pruning

Abstract

Non-Playable Character (NPC) is one of the essential elements in video games. Generally, NPCs provide challenges for players in completing missions in the game, where NPCs mean acting as enemies. The role of the enemy causes the victory rate to be one of the main goals of artificial intelligence applied to NPCs. The challenges that these NPCs provide are significant to keep players going. NPCs must be able to provide a balanced challenge like humans to have an experience that is as enjoyable as when playing with other people. The problem is the low win rate achieved by NPCs so that players can feel bored. The alpha-beta pruning algorithm is one of the decision-making algorithms that is often applied to games that require more than or equal two players. Therefore, this algorithm is suitable for applying to the object of research, namely the Triple Triad game. The Triple Triad game is a board game played by two players. The Triple Triad game was first introduced as a mini-game in the Final Fantasy VIII game. This game is a combination of card games and board games. In this study, the alpha-beta pruning algorithm was proven to increase the win rate of NPCs. It is indicated by comparing the win rate of NPCs who choose a random step, which is 17.5%, with an NPC that has applied the alpha-beta pruning algorithm, which is 55%. Therefore, there is a significant increase in the win rate. Keywords: Alpha-beta pruning; artificial intelligence; card; game; Non-Playable Character.