Procedural content generation using neuroevolution and novelty search for diverse video game levels

Michael, Beukman,; Cleghorn, Christopher Wesley; James, Steven

doi:10.1145/3512290.3528701

Cited by 8 publications

(11 citation statements)

References 38 publications

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“…PCGNN [4] is a method that uses NEAT [49] and novelty search [27] to evolve level generators. The main goal of this work is to learn reusable level generators that could quickly generate multiple different levels, as opposed to searching for single levels each time one is desired.…”

Section: Pcgnnmentioning

confidence: 99%

“…While there are numerous approaches to PCG, many of them focus on generating simple levels, and are therefore difficult to generalise to more complex settings, such as modern games. Many methods use some form of search [56], often evolutionary-based algorithms, to maximise a specific objective function [2,4,10,13]. However, it is challenging to design an objective function that is optimisable while also incentivising the generation of complex structures [54].…”

Section: A General Pcgmentioning

confidence: 99%

“…One specific method that is particularly well-suited to act as the low-level generator is PCGNN, which satisfies all of our requirements. Additional benefits include that the training time is generally quite low [4] and, while we can include detailed domain knowledge, this is not required.…”

Section: Hierarchically Composing Level Generatorsmentioning

confidence: 99%

“…However, much of the research done in the field of PCG focuses on simple, 2D tilemap games, such as Super Mario Bros, The Legend of Zelda, and maze games [4,10,13,24,42]. While these are useful test domains, the games themselves are often very simple, and do not exhibit complex structures.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach builds on a recent method, PCGNN [4], which uses neuroevolution and novelty search to evolve diverse level generators. We extend this method by hierarchically composing these generators to create large and complex structures.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchically Composing Level Generators for the Creation of Complex Structures

Michael¹,

Fokam²,

Krüger³

et al. 2023

Preprint

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Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry, and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimisable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in industry. Our work aims to address this limitation by introducing a compositional level generation method, which recursively composes simple, low-level generators together to construct large and complex creations. This approach allows for easily-optimisable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a noncompositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in Minecraft) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.

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Section: Pcgnnmentioning

confidence: 99%