Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner

Thakkar, Sarjak; Cao, Changxing; Wang, Lifan; Choi, Tae Jong; Togelius, Julian

doi:10.1109/cig.2019.8848076

Cited by 38 publications

(33 citation statements)

References 3 publications

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“…This solution adds some unpredictability. Performance evaluation compares similarity to the original game levels [27]. There is also a framework for general 2D games (mostly topdown adventures) [28].…”

Section: Related Workmentioning

confidence: 99%

“…Usefulness is defined by the safe zone and player exit dis tance criteria. Criteria were selected based on recurrence in the literature [26][27][28][29][30], game design principles, and creativity definitions [2,4,5]. If one of the constraint functions does not pass, the total fitness is multiplied by zero.…”

Section: Game Scene Procedural Generation Criteria Listmentioning

confidence: 99%

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Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

Petrovas

Baušys

2022

Applied Sciences

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The demand for automated game development assistance tools can be fulfilled by computational creativity algorithms. The procedural generation is one of the topics for creative content development. The main procedural generation challenge for game level layout is how to create a diverse set of levels that could match a human-crafted game scene. Our game scene layouts are created randomly and then sculpted using a genetic algorithm. To address the issue of fitness calculation with conflicting criteria, we use weighted aggregated sum product assessment (WASPAS) in a single-valued neutrosophic set environment (SVNS) that models the indeterminacy with truth, intermediacy, and falsehood memberships. Results are presented as an encoded game object grid where each game object type has a specific function. The algorithm creates a diverse set of game scene layouts by combining game rules validation and aesthetic principles. It successfully creates functional aesthetic patterns without specifically defining the shapes of the combination of games’ objects.

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Section: Related Workmentioning

confidence: 99%

Section: Game Scene Procedural Generation Criteria Listmentioning

confidence: 99%

Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

Petrovas

Baušys

2022

Applied Sciences

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“…The existing level generation techniques that employ DGMs have been applied to tile-based game domains wherein the levels are clearly divided into meshes and all blocks have the same size or the same unit size, and the blocks can be set by specifying the index of the level array. Therefore, the existing approaches such as [25][26][27] encode the tile-based level as an image and successfully use DGMs for image processing. However, the levels of Angry Birds are specified by real numbers, and a large number of meshes are needed to represent the exact stage.…”

Section: Angry Birdsmentioning

confidence: 99%

“…PCGML includes approaches that utilize n-gram [4] with a graphical probability model [9], generative adversarial networks (GAN) [7,26,27], and variational autoencoders (VAEs) [25]. Volz et al [27] applied the Wasserstein GAN (WGAN) to generate levels for Super Mario Bros (SMB).…”

Section: Introductionmentioning

confidence: 99%

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Level generation for angry birds with sequential VAE and latent variable evolution

Tanabe

Fukuchi

Sakuma

et al. 2021

Proceedings of the Genetic and Evolutionary Computation Conference

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Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural. CCS CONCEPTS• Computing methodologies → Neural networks; Learning latent representations;

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A Multiobjective Tuning of a Procedural Content Generator for Game Level Design via Evolutionary Algorithms

Peixoto,

Wanner,

da Cruz

2024

Communications in Computer and Information Science

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Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner

Cited by 38 publications

References 3 publications

Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

Level generation for angry birds with sequential VAE and latent variable evolution

A Multiobjective Tuning of a Procedural Content Generator for Game Level Design via Evolutionary Algorithms

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