Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Miguel, González-Duque,; Palm, Rasmus; Ha, David; Risi, Sebastian

doi:10.1109/cog47356.2020.9231548

Cited by 22 publications

(8 citation statements)

References 30 publications

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“…a reinforcement learning agent) needs to learn before being able to solve a level [18]. Different types of agents can even be used to approximate different levels of player skill [19].…”

Section: Discussion Future Work and Conclusionmentioning

confidence: 99%

Towards Objective Metrics for Procedurally Generated Video Game Levels

Michael¹,

James²,

Cleghorn³

2022

Preprint

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With increasing interest in procedural content generation by academia and game developers alike, it is vital that different approaches can be compared fairly. However, evaluating procedurally generated video game levels is often difficult, due to the lack of standardised, game-independent metrics. In this paper, we introduce two simulation-based evaluation metrics that involve analysing the behaviour of an A* agent to measure the diversity and difficulty of generated levels in a general, gameindependent manner. Diversity is calculated by comparing action trajectories from different levels using the edit distance, and difficulty is measured as how much exploration and expansion of the A* search tree is necessary before the agent can solve the level. We demonstrate that our diversity metric is more robust to changes in level size and representation than current methods and additionally measures factors that directly affect playability, instead of focusing on visual information. The difficulty metric shows promise, as it correlates with existing estimates of difficulty in one of the tested domains, but it does face some challenges in the other domain. Finally, to promote reproducibility, we publicly release our evaluation framework. 1

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“…a reinforcement learning agent) needs to learn before being able to solve a level [18]. Different types of agents can even be used to approximate different levels of player skill [19].…”

Section: Discussion Future Work and Conclusionmentioning

confidence: 99%

Towards Objective Metrics for Procedurally Generated Video Game Levels

Michael¹,

James²,

Cleghorn³

2022

Preprint

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“…QD approaches such as MAP-Elites [7,33] search for solutions along a continuum of user-defined features, making them ideal for exploration. MAP-Elites has been used for design exploration in domains such as aerodynamics [15,16,17,23,24], and game design [1,5,19,20], but has been restricted to consideration of a single objective. MAP-Elites operates by first discretizing the feature space into bins, collectively known as a map or archive.…”

Section: Exploration and Optimization With Non-objective Criteriamentioning

confidence: 99%

T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective

Gaier¹,

Stoddart²,

Villaggi³

et al. 2022

Preprint

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Real-world design problems are a messy combination of constraints, objectives, and features. Exploring these problem spaces can be defined as a Multi-Criteria Exploration (MCX) problem, whose goals are to produce a set of diverse solutions with high performance across many objectives, while avoiding low performance across any objectives. Quality-Diversity algorithms produce the needed design variation, but typically consider only a single objective. We present a new ranking, T-DominO, specifically designed to handle multiple objectives in MCX problems. T-DominO ranks individuals relative to other solutions in the archive, favoring individuals with balanced performance over those which excel at a few objectives at the cost of the others. Keeping only a single balanced solution in each MAP-Elites bin maintains the visual accessibility of the archive -a strong asset for design exploration. We illustrate our approach on a set of easily understood benchmarks, and showcase its potential in a many-objective real-world architecture case study.

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“…In the context of games, the De-LeNox system [25] used novelty search with an autoencoder to generate 2D arcade-style spaceships while Schrum et al [38] used MAP-Elites for latent space illumination in a hybrid method for Mario level generation that combined GANs with compositional pattern producing networks (CPPNs). In another hybrid approach, Gonzalez et al [12] used MAP-Elites in conjunction with gameplaying agents and Bayesian optimization in a process called Intelligent Trial & Error to generate GVG-AI levels of appropriate difficulty.…”

Section: Related Workmentioning

confidence: 99%

Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder

Sarkar

Cooper

2021

The 16th International Conference on the Foundations of Digital Games (FDG) 2021

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Several recent works have demonstrated the use of variational autoencoders (VAEs) for both generating levels in the style of existing games as well as blending levels across different games. Additionally, quality-diversity (QD) algorithms have also become popular for generating varied game content by using evolution to explore a search space while focusing on both variety and quality. In order to reap the benefits of both these approaches, we present a level generation and game blending approach that combines the use of VAEs and QD algorithms. Specifically, we train VAEs on game levels and then run the MAP-Elites QD algorithm using the learned latent space of the VAE as the search space. The latent space captures the properties of the games whose levels we want to generate and blend, while MAP-Elites searches this latent space to find a diverse set of levels optimizing a given objective such as playability. We test our method using models for 5 different platformer games as well as a blended domain spanning 3 of these games. Our results show that using MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.

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Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Cited by 22 publications

References 30 publications

Towards Objective Metrics for Procedurally Generated Video Game Levels

Towards Objective Metrics for Procedurally Generated Video Game Levels

T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective

Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder

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