On Linking Level Segments

Biemer, Colan F.; Cooper, Seth

doi:10.1109/cog51982.2022.9893705

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…When we attempted to connect the level segments built with Gram-Elites to form a larger level, we found that even if a level segment was guaranteed to be playable, it did not mean that the concatenation of two or more completable level segments was itself completable (Biemer and Cooper 2022). The solution 3 we developed had two steps: structure completion and a tree search for completability.…”

Section: On Linking Level Segmentsmentioning

confidence: 99%

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Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Biemer

2023

AIIDE

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Procedural level generation can create unseen levels and improve the replayability of games, but there are requirements for a generated level. First, a level must be completable. Second, a level must look and feel like a level that would exist in the game, meaning a random combination of tiles that happens to be completable is not enough. On top of these two requirements, though, is the player experience. If a level is too hard, the player will be frustrated. If too easy, they will be bored. Neither outcome is desirable. A procedural level generation system has to account for the player's skill and generate levels at the correct difficulty. I address this issue by showing how a Markov Decision Process can be used as a director to assemble levels tailored to a player's skill level, but I've only demonstrated that my approach works with surrogate agents. For my thesis, I plan to build on my past work by creating a full roguelike and platformer and running two player studies to validate my approach.

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Section: On Linking Level Segmentsmentioning

confidence: 99%

“…A full level is assembled by concatenating level segments together. This led to my second paper (Biemer and Cooper 2022) which linked level segments together to form a larger level. A linking algorithm was necessary to ensure that an assembled level was completable and matched the style of the target game.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Biemer

2023

AIIDE

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Segment-wise Level Generation using Iterative Constrained Extension

Mao,

Cooper

2023

2023 IEEE Conference on Games (CoG)

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Evaluating the Expressive Range of Super Mario Bros Level Generators

Schaa,

Barriga

2024

Algorithms

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Procedural Content Generation for video games (PCG) is widely used by today’s video game industry to create huge open worlds or enhance replayability. However, there is little scientific evidence that these systems produce high-quality content. In this document, we evaluate three open-source automated level generators for Super Mario Bros in addition to the original levels used for training. These are based on Genetic Algorithms, Generative Adversarial Networks, and Markov Chains. The evaluation was performed through an Expressive Range Analysis (ERA) on 200 levels with nine metrics. The results show how analyzing the algorithms’ expressive range can help us evaluate the generators as a preliminary measure to study whether they respond to users’ needs. This method allows us to recognize potential problems early in the content generation process, in addition to taking action to guarantee quality content when a generator is used.

show abstract

On Linking Level Segments

Cited by 3 publications

References 17 publications

Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Dynamic Difficulty Adjustment via Procedural Level Generation Guided by a Markov Decision Process for Platformers and Roguelikes

Segment-wise Level Generation using Iterative Constrained Extension

Evaluating the Expressive Range of Super Mario Bros Level Generators

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