BiLSTM and dynamic fuzzy AHP-GA method for procedural game level generation

Abstract

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.

Research on Multi-Equipment Collaborative Scheduling Algorithm under Composite Constraints

“…For optimized scheduling of the production process, scholars have proposed many genetic algorithms, including particle swarm algorithms, NSGA algorithms, super-heuristic algorithms, hybrid algorithms, etc. [6][7][8][9][10][11]. Zhou et al introduced new operators for dynamic programming based on the decomposed multi-objective evolution algorithm and verified its superiority through experiments [12].…”