The use of games in daily life, especially in education, has been in an incline during the COVID-2019 pandemic. Thus, game-based learning environments have caused an increase in the need of game contents, but generation of the game contents and levels is a time-consuming and costly process. Generated game contents and levels should be balanced, dense, aesthetic and reachable. Also, the time as well as the costs spent should be decreased. In order to overcome this problem, automatic and intelligent game content and level generation methods have emerged, and procedural content generation (PCG) is the most popular one of these methods. Artificial intelligence techniques are used for procedural game level generation instead of traditional methods. In this study, bidirectional long short-term memory (BiLSTM) and fuzzy analytic hierarchy process-genetic algorithm (FAHP-GA) methods were used to generate procedural game levels. This proposed hybrid system was used in a developed educational game as a case study to create game levels. The performance of the proposed study was compared to the other multi-criteria decision-making (MCDM) methods, and also further statistical analyses were investigated. The results showed that the BiLSTM-based FAHP-GA method can be used for procedural game level generation effectively.

在 COVID-2019 大流行期间，游戏在日常生活中的使用，尤其是在教育中的使用一直呈上升趋势。因此，基于游戏的学习环境导致对游戏内容的需求增加，但游戏内容和关卡的生成是一个耗时且昂贵的过程。生成的游戏内容和关卡应该是平衡的、密集的、美观的和可达的。此外，应该减少所花费的时间和成本。为了克服这个问题，出现了自动和智能的游戏内容和关卡生成方法，其中程序化内容生成（PCG）是其中最流行的一种。人工智能技术用于程序化游戏关卡生成，而不是传统方法。在这项研究中，双向长短期记忆（BiLSTM）和模糊层次分析过程遗传算法（FAHP-GA）方法被用来生成程序游戏关卡。该提议的混合系统被用于开发的教育游戏中，作为创建游戏关卡的案例研究。将拟议研究的性能与其他多标准决策 (MCDM) 方法进行了比较，并进行了进一步的统计分析。结果表明，基于 BiLSTM 的 FAHP-GA 方法可以有效地用于程序化游戏关卡生成。并进行了进一步的统计分析。结果表明，基于 BiLSTM 的 FAHP-GA 方法可以有效地用于程序化游戏关卡生成。并进行了进一步的统计分析。结果表明，基于 BiLSTM 的 FAHP-GA 方法可以有效地用于程序化游戏关卡生成。