Procedural Map Generation for 'Splatted': Enhancing Player Experience through Genetic Algorithms and AI Finite State Machines in a Snowball Throwing Game


  • Lukky Hariyanto Institut Sains dan Teknologi Terpadu Surabaya
  • Hendrawan Armanto Institut Sains dan Teknologi Terpadu Surabaya



Game, Genetic Algorithm, Map, Procedural Map Generation


Games, a now extremely prevalent form of global entertainment, have emerged as a leading industry in the entertainment media, surpassing other entertainment media such as books, films, and music. However, game development is a complex endeavor, requiring a diverse set of talents to create a decent game for people to enjoy. Some of the talents needed to create a good game is a game designer, which dictates how a player can interact with the world, a writer, which pours a meaningful story inside said world, and a composer, which uses music to elevate the emotions evoked by the game and its events. With that being said, this research aims to streamline the creation process of the game designers, specifically the level designers by focusing on procedural map generation and artificial intelligence to create a map that is in a playable state for the players to play in. Procedural map generation, facilitated by a genetic algorithm inspired by Darwin's evolutionary theory, expedites the level design process. The research explores two types of map generation—tile-based and template-based, each with distinct advantages and disadvantages. Through user acceptance tests and expert-level analysis, it is evident that the genetic algorithm performs effectively, achieving a noteworthy level of player satisfaction.

Author Biography

Hendrawan Armanto, Institut Sains dan Teknologi Terpadu Surabaya

Head of Research and Community Service  Kepala Lembaga Penelitian dan Pengabdian kepada Masyarakat
Head of Academic Bureau Kepala Biro Admnistrasi Akademik


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How to Cite

L. Hariyanto and H. Armanto, “Procedural Map Generation for ’Splatted’: Enhancing Player Experience through Genetic Algorithms and AI Finite State Machines in a Snowball Throwing Game”, INSYST, vol. 6, no. 1, pp. 24–31, Apr. 2024.