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Using estimation of distribution algorithm for procedural content generation in video games

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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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Author information

Authors and Affiliations

  1. Department of Computer Engineering, University College of Nabi Akram, Tabriz, Iran

    Arash Moradi Karkaj

  2. Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran

    Shahriar Lotfi

Authors
  1. Arash Moradi Karkaj
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  2. Shahriar Lotfi
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Corresponding author

Correspondence to Shahriar Lotfi.

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Handling Editor: Sebastian Risi.

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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

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