Abstract
Content generation is one of the major challenges in the modern age. The video game industry is no exception and the ever-increasing demand for bigger titles containing vast volumes of content has become one of the vital challenges for the content generation domain. Conventional game development as a human product is not cost efficient and the need for more intelligent, advanced and procedural methods is evident in this field. In a sense, procedural content generation (PCG) is a Non-deterministic Polynomial-Hard optimization problem in which specific metrics should be optimized. In this paper, we use the Estimation of Distribution Algorithm (EDA) to optimize the task of PCG in digital video games. EDA is an evolutionary stochastic optimization method and the introduction of probabilistic modeling as one of the main features of EDA into this problem domain is a reliable way to mathematically apply human knowledge to the challenging field of content generation. Acceptable performance of the proposed method is reflected in the results, which can inform the academia of PCG and contribute to the game industry.
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Appendix A. Design elements
Appendix A. Design elements
Flat surface | FLAT | Start and end of string | START, END |
---|---|---|---|
coin | COINS | Block variations and combinations | BLOCK_PP BLOCK_CC BLOCK_EE BLOCK_PC BLOCK_PE BLOCK_CE |
pipe | PIPE | ||
piranha | PIPE_PIRANHA | ||
canon | CANNON | ||
gap | GAP | Variety of enemies (most of the methods limit the enemy type) | GOOMBA REDTURTLE GREENTURTLE SPIKY GOOMBA_WINGED REDTURTLE_WINGED GREENTURTLE_WINGED SPIKY_WINGED |
Increase in ground level | GROUND_UP | ||
Decrease in ground level | GROUND_DOWN | ||
Stairs going up | STAIRS_UP | ||
Stairs going down | STAIRS_DOWN | ||
Variations and combinations of various blocks to add variety to patterns | GOOMBA_BLOCK_PP, GOOMBA_BLOCK_CC, GOOMBA_BLOCK_EE GOOMBA_BLOCK_PC, GOOMBA_BLOCK_PE, GOOMBA_BLOCK_CE BLOCK_EE_GREENTURTLE, REDTURTLE_BLOCK_PE, GREENTURTLE_BLOCK_PP BLOCK_PC_GREENTURTLE, REDTURTLE_BLOCK_CE, GREENTURTLE_BLOCK_CC BLOCK_PE_GREENTURTLE, BLOCK_PP_GOOMBA, GREENTURTLE_BLOCK_EE BLOCK_CE_GREENTURTLE, BLOCK_CC_GOOMBA, GREENTURTLE_BLOCK_PC BLOCK_PP_REDTURTLE, BLOCK_EE_GOOMBA, GREENTURTLE_BLOCK_PE BLOCK_CC_REDTURTLE, BLOCK_PC_GOOMBA, GREENTURTLE_BLOCK_CE BLOCK_EE_REDTURTLE, BLOCK_PE_GOOMBA, REDTURTLE_BLOCK_PP BLOCK_PC_REDTURTLE, BLOCK_CE_GOOMBA, REDTURTLE_BLOCK_CC BLOCK_PE_REDTURTLE, BLOCK_PP_GREENTURTLE, REDTURTLE_BLOCK_EE BLOCK_CE_REDTURTLE, BLOCK_CC_GREENTURTLE, REDTURTLE_BLOCK_PC |
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Moradi Karkaj, A., Lotfi, S. Using estimation of distribution algorithm for procedural content generation in video games. Genet Program Evolvable Mach 23, 495–533 (2022). https://doi.org/10.1007/s10710-022-09442-y
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DOI: https://doi.org/10.1007/s10710-022-09442-y