Abstract
The game development process is becoming a more detailed structure every day. The applications of artificial intelligence (AI), which is a comprehensive information technology, have been closely related to game technologies. In this study, the levelling process of a 2-dimensional (2D) platform game was investigated. The game developed and called “Renga” has a basic gameplay. Game data has been processed through an artificial neural network (ANN), k-nearest neighbour, decision and random tree algorithms and deep learning model that is trained with gameplay and user information. The classification process with the output data provides results for the next game level. In this way, the most effective playability impression that the developers offer to the game users has been created according to game. Furthermore, the variety of difficulty calculated with dynamic data by the user is provided by Renga, in which new sections/levels are created with user-specific assets. Thus, the most efficient gaming experience has been transferred to the users.
Highlights
AMONG the platforms, different types of games that can attract the attention of their target users are increasing and improving
There are various studies that are designed by machine learning [12,13,14,15,16], probabilistically techniques [17], Procedural Content Generation (PCG) [18,19,20] for dynamic game levelling
Game technologies have developed into different categories. Productions such as Super Mario, Contra and Metal Slug have not lost their popularity despite the emergence of 3D games, and a new era of video games began with platform games such as Limbo, Inside, Ori and the Blind Forest
Summary
There are various studies that are designed by machine learning (genetic algorithm, artificial neural network, support vector machine) [12,13,14,15,16], probabilistically techniques [17], Procedural Content Generation (PCG) [18,19,20] for dynamic game levelling. Game levels for Renga, which was developed as a 2D platform game, have been identified based on artificial intelligence In this context, perception of difficulty in infinite games has been examined, balanced the player performance of Renga game and different game levels have been designed according to player ability. The most efficient gaming experience has been transferred to the users
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