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

Murat İnce1

  • 1Vocational School of Technical Sciences, Isparta University of Applied Sciences, Cunur West Campus, Isparta, 32200 Turkey.

Neural Computing & Applications
|June 28, 2021
PubMed
Summary

This study introduces an effective hybrid system using Bidirectional Long Short-Term Memory (BiLSTM) and fuzzy analytic hierarchy process-genetic algorithm (FAHP-GA) for procedural game level generation. This AI-driven approach significantly reduces time and cost in creating balanced and engaging game content for educational games.

Keywords:
BilstmFuzzy analytic hierarchy processGame level generationGenetic algorithm

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Area of Science:

  • Artificial Intelligence
  • Game Design
  • Educational Technology

Background:

  • The COVID-19 pandemic accelerated the adoption of game-based learning, increasing demand for game content.
  • Generating game content and levels is traditionally time-consuming and expensive, requiring improvements in efficiency and cost-effectiveness.
  • Procedural Content Generation (PCG) offers automated solutions for creating game levels, leveraging artificial intelligence techniques.

Purpose of the Study:

  • To develop and evaluate a hybrid AI system for automated procedural game level generation.
  • To address the need for efficient, cost-effective, and high-quality game content creation in educational settings.
  • To compare the performance of the proposed hybrid method against existing multi-criteria decision-making (MCDM) approaches.

Main Methods:

  • A hybrid system combining Bidirectional Long Short-Term Memory (BiLSTM) and Fuzzy Analytic Hierarchy Process-Genetic Algorithm (FAHP-GA) was developed.
  • The proposed system was implemented in a custom-developed educational game to generate game levels.
  • Performance evaluation involved comparisons with other MCDM methods and statistical analyses.

Main Results:

  • The BiLSTM-based FAHP-GA hybrid method demonstrated effectiveness in procedural game level generation.
  • The developed system successfully created game levels for the educational game case study.
  • Statistical analyses confirmed the viability and performance of the proposed approach.

Conclusions:

  • The BiLSTM-based FAHP-GA hybrid system is a viable and effective solution for procedural game level generation.
  • This AI-driven approach offers a promising method to reduce the time and cost associated with game content creation.
  • The study highlights the potential of hybrid AI techniques in enhancing educational game development.