An Artificial Neural Network-Based Finite State Machine for Adaptive Scenario Selection in Serious Game

Abstract

Serious game is one of the pedagogical media capable of transferring knowledge to its players. This game genre requires a support system that adaptively selects the appropriate scenario for players to increase their interest and comfort. Therefore, this study proposed an adaptive scenario selection (ASS) system using a finite state machine based on an artificial neural network (ANN). The game scenario is selected by ASS based on five player preferences, including work, hobbies/interests, origin, group members, and repetition. Furthermore, the multi-layer perceptron (MLP) architecture was used in the scenario selection process for the proposed ANN method. The experimental stage was carried out using the theme of travel in several tourism destinations in Batu City, East Java, Indonesia. The experimental results show that ASS succeeded in generating adaptive game scenario choices for players based on their preference data with an accuracy of 67.25%.