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Cellular automata did not produce a connected map in one author’s 20-run test, but that does not mean the method never works. In turingrtss’s comparison, four dungeon generators ran on the same 80×40 grid: BSP, cellular automata, random walk, and room placement. The first, third, and fourth produced connected maps in all 20 runs; the tested cellular-automata implementation produced none. That is a useful warning about checking reachability—not a universal verdict on the algorithm.
What did the four-algorithm test find?
The result depends on what you need from a dungeon. In turingrtss’s reported comparison, BSP and room placement were the fastest methods, cellular automata produced the most open space, and random walk produced the shortest measured path. Only the tested cellular-automata maps failed the author’s connectivity check in every run.
The author ran each method 20 times on an 80×40 grid and reported these averages and rates:
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| Method | Open space | Connected results | Rooms | Path length | Generation time |
|---|---|---|---|---|---|
| BSP tree | 42.1% | 100% | 1.0 | 105 steps | 0.88 ms |
| Cellular automata | 55.8% | 0% | 15.2 | 78 steps | 52.8 ms |
| Random walk | 35.0% | 100% | 1.0 | 73 steps | 274.7 ms |
| Room placement | 18.9% | 100% | 1.0 | 81 steps | 0.29 ms |
These are turingrtss’s measurements for that implementation and setup, not independently reproduced results or portable benchmarks. The article reports the path lengths in steps and the generation times in milliseconds. Its figures should be read as a comparison of those particular runs, not as guaranteed values for other code, hardware, settings, or map sizes.
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The table also does not establish that one metric is always preferable. More open space can suit a broad cave, while a lower open-space ratio might fit a compact layout. A shorter measured path can create a more direct route, but says little on its own about optional branches, pacing, or whether the map suits a particular game.
Why did cellular automata fail to connect these maps?
Cellular automata typically begin with a noisy arrangement of floor and wall cells, then update each cell according to a rule based on nearby cells. Repeating the rule tends to smooth the initial pattern into more cave-like regions. The smoothing process shapes local texture; it does not automatically guarantee that all floor cells belong to one traversable region.
As turingrtss put it, “The big surprise: cellular automata never produces a connected map.” In context, “never” describes the author’s tested implementation: zero connected maps across 20 runs on an 80×40 grid. It is not evidence that every cellular-automata rule set, parameter choice, or repair process must fail.
The practical distinction is between appearance and reachability. A map may look like one cave while containing isolated pockets, or it may have several visible cave areas joined by narrow passages. If the game requires the player to reach every room or objective, a visual inspection is not a substitute for checking the actual floor-cell connections.
How to handle disconnected areas
After generating a cellular-automata map, identify its connected floor components. Then decide what the game requires: keep only the largest component, connect the components with corridors, or regenerate the map. The right choice depends on whether isolated areas are acceptable, whether the player must visit every objective, and how much post-processing fits the desired cave style.
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The 2020 SBGames conference paper likewise distinguishes organic cave generation from guaranteed connectivity: it notes that connectivity between regions is not ensured in its cave-system mode of drunkard’s walk. That is a related caveat, not a claim that random walk failed in turingrtss’s experiment; that author reported connected results in all 20 random-walk runs.
How do BSP, random walk, and room placement differ?
The methods produce different structures because they solve different parts of the layout problem. BSP organizes space into regions; random walk carves passages through filled space; cellular automata shapes a noisy grid; and room placement sets locations for rooms that still need a connection plan.
BSP: partition the space, then connect rooms
Binary space partitioning recursively divides a map into smaller regions, places rooms within those regions, and links the rooms. Its structure makes it a natural starting point when a design needs distinct rooms and corridors rather than a continuous cave. In the reported comparison, BSP reached 100% connectivity, averaged 42.1% open space, and generated in 0.88 ms.
Its structure also constrains what the generator can produce. Partition depth, room dimensions, and the way rooms are linked all influence the result. If many maps feel too similar, the relevant tuning is not simply “make it random”: the partition and connection choices shape the range of layouts.
Random walk: carve as an agent wanders
In a random-walk generator, one or more agents move through the map and carve floor as they go. This can create winding, organic passages. The outcome depends on factors such as the starting positions, number of agents, and stopping rules; connectivity is not inherent in every possible variant.
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In turingrtss’s comparison, random walk was connected in all 20 runs, had the shortest reported path length at 73 steps, and took 274.7 ms per generation in that setup. Those numbers do not make random walk universally slower or its paths universally shorter: they describe that implementation’s tested maps.
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Room placement puts rooms at randomized positions, often giving the developer direct control over room count and dimensions. But placing rooms is not the same as generating a complete dungeon. A separate corridor strategy must decide which rooms connect, how routes are laid out, and whether every required room can be reached.
The 2019 SBGames survey catalogs combinations of room-generation and corridor-generation approaches, underscoring that the connecting stage is part of the design rather than an incidental detail. In the reported test, room placement had 100% connectivity, 18.9% open space, and the fastest time at 0.29 ms. Those results belong to the author’s full implementation, including whatever connection approach it used; they should not be read as properties of room placement in isolation.
Which algorithm should you choose for your game?
Choose based on the layout’s intended play experience and the checks your game needs to pass. The most useful first question is whether you want rooms and corridors or a more continuous cave-like space.
- For separated rooms and conventional corridors: start with BSP, or place rooms and add a deliberate corridor-connection method.
- For cave texture: consider cellular automata or random-walk carving, then check connected components and repair them if progression requires it.
- For controlled room dimensions or counts: room placement gives direct control over the rooms, but plan its corridor stage as part of the generator.
- For reliable progression with optional routes: measure reachability, route structure, and path redundancy instead of judging the map only by its appearance.
These are starting points, not strict categories. A generator can combine methods—for example, use one method to create rooms and another to connect them. The 2019 SBGames survey’s treatment of room and corridor generation as combinable stages is a useful reminder that a dungeon generator need not be a single algorithm.
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How should you evaluate a generated dungeon?
Connectivity is essential when the game requires reachability, but it is only one measure of quality. A useful evaluation checks whether the map works for its intended play, not just whether the generator finished or produced an attractive image.
Check reachability and progression
Identify the floor regions and test whether the player’s start, exits, and required objectives lie in the intended connected area. If some regions are deliberately optional or inaccessible, define that explicitly rather than counting every disconnected cell as an automatic failure. The check should reflect the game’s rules about doors, keys, one-way passages, and other traversal conditions; a simple floor-to-floor connection test cannot represent those mechanics unless they are included.
Measure routes, not just map shape
Path length can help compare how direct a route is, but it does not tell you whether alternate routes exist or whether the level feels too linear. In a separate 2025 HSE University thesis abstract, Maksim Kudasov reports 30–50% optional-path redundancy for a distinct BSP-plus-ASP pipeline. That figure is not part of turingrtss’s four-way test, nor a general target for every game; it illustrates that path redundancy can be evaluated as its own design property.
The same 2025 thesis evaluates mission and map linearity, leniency, and path redundancy. These criteria help distinguish a technically connected map from one that offers the intended amount of choice, recovery room, or route variation.
Compare performance on the target setup
Generation time is meaningful only alongside the conditions that produced it. To compare your own implementations, use the target grid size, language and runtime, hardware, parameters, and any post-processing the game will actually perform. Run multiple seeds and record the same metrics for each method; the original article’s 20 runs per algorithm provide a sample, but do not establish a universal ranking.
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Keep the measurement stages consistent. If one method’s timing includes component detection and corridor repair while another’s does not, the figures are not measuring equivalent work. Likewise, compare maps with the same connectivity requirements and report failures rather than silently discarding them.
What should you take away from the comparison?
The experiment is most useful as a lesson in matching algorithms to constraints. In this implementation, cellular automata yielded the greatest open-space percentage but no connected maps, while three other approaches connected every reported run. That result shows why reachability needs an explicit test; it does not establish that cellular automata is unusable.
BSP is a sensible starting point for partitioned rooms and corridors, random walk and cellular automata can provide organic cave-like structure, and room placement offers direct control over room layouts. In every case, evaluate the complete pipeline—including the connection and repair stages—against the movement and progression rules of the game.
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Frequently Asked Questions
Can I use cellular automata for a dungeon that must be fully traversable?
Yes, if your generation pipeline includes a reachability check and a response to disconnected regions. Whether to join components, discard smaller areas, or regenerate depends on whether the design permits optional or unreachable spaces.
Does room placement automatically create corridors between rooms?
No. Room placement determines where rooms go; a separate corridor-generation step is needed to define routes between them. The corridor strategy can materially change the final dungeon.
How many optional paths should a procedural dungeon have?
There is no universal percentage in the cited material. A 2025 HSE University thesis abstract reports 30–50% optional-path redundancy for its separate BSP-plus-ASP pipeline, but a suitable amount depends on the game’s intended route choice and pacing.
Why might my benchmark results differ from the published comparison?
The reported values come from one author’s implementation, parameters, grid size, and test environment. Different hardware, runtimes, map settings, connection logic, or timing boundaries can change both speed and layout metrics.
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