Modification of Game Agent using Genetic Algorithm in Card Battle game

Abstract

Abstract Game Agent is currently being developed to be an opponent in the game, including games with the Card Games genre. Game Agents on traditional Card Games - such as poker, dominoes, or mahjong cards - have abilities that depend on the value of the cards, but the ability of these Game Agents will not be optimal if used in the Card Battle game. This is because Card Battle has many attributes that must be processed to become opponents. Therefore, this research modifies the Game Agent with Genetic Algorithm to optimize the playing ability of the Game Agent in Card Battle. The computational stages and fitness formula of the Genetic Algorithm are adjusted to the Card Battle rules to increase the computational speed of the Genetic Algorithm. The results of this study prove that Game Agent modification of Genetic Algorithm provides a more optimal playing ability than its predecessor algorithm. Game Agent that has been modified has several abilities that are not owned by the previous Game Agent, such as issuing cards to attack opponents directly and storing SP (Summon Points) they have.