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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A generated level can be technically completable and still feel wrong for the game. To fix it, identify the specific layout or difficulty problem, make a targeted edit, and test the result again. Treat connectivity and simulated-player scores as diagnostic clues—not proof that a level is fun, fair, or appropriately challenging for people.
Start by naming what feels wrong
Before changing the level, translate “wrong” into an observable problem. A vague instruction to regenerate can discard working parts along with the problem area, while a specific diagnosis points to an edit you can evaluate.
- For a layout problem: Check whether important regions connect, whether the route supports the level’s objective, and whether the arrangement fits the game’s established level structure.
- For a difficulty problem: Identify which demand is too high or too low, such as route complexity, obstacle placement, or time pressure.
- For a game-fit problem: Identify what clashes with the game’s design language—such as an implausible room arrangement, an out-of-place obstacle pattern, or a route that does not suit the objective.
These checks help focus an edit; they are not universal pass/fail thresholds. What counts as a suitable route or challenge depends on the game and its intended players.
Check validity before judging quality
First establish whether the level can be completed and whether its structure is coherent. Then judge whether it looks and plays like it belongs in this game. Those are separate tests: a valid tile arrangement can still fail as a designed level.
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Colan F. Biemer put the distinction plainly in a 2023 doctoral-consortium abstract: “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.” Read Biemer’s abstract.
Use a targeted, iterative edit
Refinement works best as a loop: inspect the current level, collect structured feedback, plan a constrained change, and evaluate the updated version. Change the route, a room, obstacle placement, or a relevant generation parameter rather than replacing the entire level when the failure is localized.
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The Agentic PCG project describes an interactive approach in which an agent inspects a game state, plans an edit, and evaluates it using feedback from the environment. Its examples include direct structural checks—such as tile counts, connectivity, and solvability—and behavior-based feedback from a simulated agent. A designer can use the same pattern manually:
- Inspect: Locate the route, room, obstacle, or parameter tied to the symptom.
- Choose a constrained edit: Change the smallest relevant part while preserving elements that already work.
- Recheck: Test completion, connectivity, or the relevant gameplay behavior after the change.
- Compare: Keep the game and evaluation method consistent when comparing the old and new versions.
These checks can reveal structural problems, but a simulated agent’s behavior is not a substitute for human feedback about perceived challenge or quality. The project presents a workflow, not proof that every AI model or game will improve through it. See the Agentic PCG project.
If the level is too hard or too easy, find the source of the challenge
Difficulty adjustment should respond to evidence about the player and the demands of the level, not just a single automated score. Biemer’s 2023 work describes using a Markov decision process as a director to assemble levels tailored to player skill. The demonstrated approach used surrogate agents, however, and the abstract said player studies were planned; it does not establish that the method improves human players’ experience.
A 2015 study of difficulty-adjusted Spelunky levels used direct and indirect player feedback. Its record reports that most users appreciated online adaptation but were especially critical of the game becoming easier at any time. That game-specific finding is a reason to be cautious about automatic easing: reducing difficulty can conflict with the challenge players expect. Check changes with players and preserve the intended experience. Read the Spelunky study record.
When adjusting difficulty, state what you intend to change—such as time pressure, obstacle density, or route demand—then check whether the revised level remains completable and fits the game. Automated measures can help identify what changed; player feedback is needed to assess how the challenge feels.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Expose controls that map to meaningful design choices
If the generator offers controls, useful parameters should correspond to features a designer can reason about, rather than leaving every decision behind an opaque regenerate button. A preliminary dungeon-crawler study compared three levels of player influence over 22 level-generation parameters and found significantly higher reported autonomy in the high-control condition.
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The authors also called for further work to disentangle agency from challenge. That result does not show that more controls automatically make levels better, nor can it be generalized to every game. Use adjustable parameters to make deliberate edits possible, then evaluate the resulting level. Read the study on player influence over procedurally generated levels.
Compare revised levels on consistent criteria
When deciding whether an edit helped, compare versions using the same game context and evaluation method. Consider each dimension separately instead of collapsing them into one score:
- Validity: Can the level be completed?
- Structure: Are the important areas connected, and does the route support the objective?
- Game fit: Does the layout look and play like a level from this game?
- Intended challenge: Does it ask the intended player for the right kind of effort?
- Player experience: How do people describe the difficulty, quality, and degree of control they feel?
The cited work does not establish shared numeric thresholds for these criteria. Use measures and feedback to diagnose the specific game you are designing, rather than treating an automated score as a universal definition of a good level.
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