Procedural dungeon generation for a 2D top-down game

Weeks, Michael; Davis, J.R.

doi:10.1145/3476883.3520214

Cited by 3 publications

(1 citation statement)

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“…5 One approach is to have an agent solve a level and then solve the level again, but requirements like keys are removed. The difference between path lengths can be used as an indicator of difficulty (Weeks and Davis 2022). In my work, though, I need to validate a measure of difficulty before using it.…”

Section: Future Workmentioning

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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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: Future Workmentioning

confidence: 99%