Overview
Constraint-based generation models content choices as variables with allowed values and constraints. Search, propagation, or repair steps then find assignments that produce valid maps, layouts, puzzles, or patterns.
CoreSource checked
Constraint-Based Generation
Generates content by searching for assignments that satisfy design constraints.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Procedural Generation
- Difficulty
- Advanced
- Family
- Constraint Generation
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 1
- Relationships
- 4
- Code example
- Ready
- Visualization
- Ready
Core Concepts
variable assignmentconstraint propagationbacktrackingvalid layout
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(1)
Average
O(exponential)
Worst
O(exponential)
Space
O(variables)
When to Use
- Use when validity rules can be stated clearly enough to check or propagate.
- Use when rejecting invalid output after generation would waste too much work.
When Not to Use
- Avoid when the desired style is hard to express as constraints.
- Avoid when constraint solving would be too slow for the generation budget.
What It Does
- Represents generation choices as variables
- Prunes invalid choices with constraints
- Searches or repairs until a valid structure is produced
Use Cases
- Puzzle and level generation
- Tile maps with adjacency or placement rules
- Layouts that must satisfy hard design constraints
Inputs
- Seed
- Generation parameters
- Constraint rules
Outputs
- Generated map, texture, layout, or pattern
Key Formulas (1)
Constraint check
all constraints assignment−compatible = true
all constraints assignment-compatible = trueAccepts only partial or complete assignments that satisfy the rules considered so far.
Pseudocode
- 1.Define variables, domains, and constraints
- 2.Choose an unassigned variable
- 3.Try a value that satisfies current local constraints
- 4.Propagate consequences to neighboring variables
- 5.Backtrack or repair when a contradiction appears
- 6.Return the completed assignment
Canonical Example
- Input
- 3 rooms where each must connect to at least one corridor and no two treasure rooms touch
- Output
- a layout satisfying both constraints
- Explanation
- The generator assigns room types and rejects or repairs placements that violate the rules.
Implementation Notes
- Separate constraint definitions from the search strategy.
- Log contradictions with enough state to reproduce them.
- Use small grids or layouts to test each constraint independently.
Common Pitfalls
- Too-strict constraints can make generation fail or backtrack excessively.
- Too-weak constraints produce valid but uninteresting output.
- Random choices without recorded seeds make failures hard to reproduce.
Interactive Mechanism Walkthrough
Spatial Primitive & Sweep-Line CanvasShow the generation state evolving from seed values to the final artifact.
Step 1 of 6: Define variables, domains, and constraints
procedural-generation-mechanismNotes
Most constraint generators need both validity checks and quality checks.
Sources (1)
What each reference supports for this entry.
Procedural Content Generation in Games
BookProcedural-generation reference supporting Constraint-Based Generation constraint solving, constructive generation, terrain, dungeon, or stochastic content workflow.
Open sourceExplore Next
Good next jumps from the relationship graph, ranked for useful discovery.
UsesConstraint Satisfaction Backtracking
Optimization · Core
Constraint-based generation often uses CSP-style backtracking or propagation.
Compare with thisRelationships
View full relationship pageConstraint-based generation often uses CSP-style backtracking or propagation.
GeneralizesWave Function Collapse
Wave Function Collapse is a specific constraint-propagation style generator.
Tags
Procedural GenerationConstraint SolvingSearchGeneration