Procedural content generation of terrain using evolutionary algorithms.

1. Evolving Patch-
based Terrains for
use in Video Games
William Raffe, Fabio Zambetta, Xiaodong Li
RMIT University, Melbourne, Australia

2. Outline
Background
Procedural Terrain Generation.
Using EA in Content Generation.
Approach
Patch-based Terrain Generation
• Extracting patches, re-combining patches, and terrain generation
parameters.
Evolutionary Algorithm
• Genetic representation and operators.
• Parent and Gene Selection.
Results
Sample run.
Capabilities in generating terrain for games.
Future Work
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

3. Procedural Terrain
Generation
The creation of virtual terrain through algorithmic
means.
Reduces the time and skill to create terrain.
Focus on game maps
Reduce production costs.
Increase replay value.
Popular techniques include:
Fractals
Erosion simulation
Noise generators
Delaunay and Voronoi diagrams
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

4. Height-map Terrains
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

5. Height-map Terrains
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

6. Why use EA?
Iterative evolutionary process allows for maps to be refined over
time.
Feedback from users or fitness functions direct the terrain
generation process.
More control
• Generate-and-test vs Constructive.
Evolutionary algorithms successfully used on other types of
game content.
Euphoria character animation
Search-based Procedural Content Generation (SBPCG).
EA popular option for SBPCG.
Has been used to change game content for each player based on
their individual preferences and how they play the game.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

7. Patch-based Approach
Extract patches from sample terrains.
Initial population can either be:
The sample terrains themselves.
OR
Constructed of randomly selected patches.
Conduct Two-Level interactive evolution.
Parent Selection
Gene Selection
Genetic operators create children by swapping individual
patches of the selected parents.
Repeat interactive evolution on new generation.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

8. Extracting Patches
Program takes 8 sample terrains.
Sample terrains are divided into uniform
sized patches.
Extra height-map data on all sides of each patch is
extracted to allow for overlapping.
Each patch is:
Stored as a 2D array of height values (i.e. a small
height-map).
Placed in a global patch database.
Given a unique ID that is referenced when
constructing new terrains.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

9. Recombining Patches
(Roof-Tiling)
Choose a starting patch
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

10. Recombining Patches
(Roof-Tiling)
Choose a starting patch
Choose the next patch and overlap
it with the one before it.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

11. Recombining Patches
(Roof-Tiling)
Choose a starting patch
Choose the next patch and overlap
it with the one before it.
Keep adding patches until an entire
row is complete.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

12. Recombining Patches
(Roof-Tiling)
Choose a starting patch
Choose the next patch and overlap
it with the one before it.
Keep adding patches until an entire
row is complete.
Repeat this to create a second row.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

13. Recombining Patches
(Roof-Tiling)
Choose a starting patch
Choose the next patch and overlap
it with the one before it.
Keep adding patches until an entire
row is complete.
Repeat this to create a second row.
Overlap the second row with the
first.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

14. Recombining Patches
(Roof-Tiling)
Choose a starting patch
Choose the next patch and overlap
it with the one before it.
Keep adding patches until an entire
row is complete.
Repeat this to create a second row.
Overlap the second row with the
first.
Repeat the process of creating
rows and adding them to the row
before until a full terrain is made.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

15. Stitching Patches
Patches are overlapped to
ensure a smooth transition
from one patch to the next.
Height values in the overlap
region are interpolated
between the two patches.
The height of a vertex is
weighted towards one patch or
the other based on how far
through the overlap region it is.
Cubic Spline Interpolation is
used to smooth the entries into
the overlap region.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

16. Terrain Parameters
Number of Patches:
Less Patches: Larger patch size. Ensures smooth
flow in terrain but does not allow for variety.
More Patches: Smaller patch size. Allows for control
over finer details but higher chance jagged terrain.
Overlap size:
Large Overlap: Can blend patches together to make
rolling terrain but lose feature details in each patch.
Small Overlap: Creates hard edges which are good
for cliff faces but makes the boarders of each patch
more obvious.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

17. Patch and Overlap Size
Increase
Number of Patches = 4 (2x2) number of
Overlap Size = 80% patches
Number of Patches = 81 (9x9)
Reduce
Overlap
Size
Overlap Size = 20%
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

18. EA - Representation
Genotype Representation: Patch Map
A 2D array of patch ID numbers (a Patch Map). Each patch
ID corresponds to a patch in the current dataset of patches
(extracted from sample terrains at start-up).
Dimensions of array reflect how many patches are used to
construct each terrain.
13 5 45
22 15 9 Terrain made up of 9 patches (3x3)
34 18 40
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

19. EA - Crossover
Uniform Crossover:
One parent chosen as base parent.
Each patch given a randomly generated probability to crossover
If this value is less than the Crossover Rate parameter, patch is
switched for patch in same position on other parent, otherwise the
patch is kept where it is.
Crossover Rate between 0 and 1:
Due to randomly chosen base parent, a rate over 0.5 is equivalent
to that under 0.5.
We mostly used a crossover rate of 0.5 – patches from both
parents have equal chance of being present in each child.
Crossover rate of 0.2. First parent (white) is base parent.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

20. Mutation
Each patch given probability to mutate and
is compared to the Mutation Rate
parameter, same as crossover.
If the patch is to be mutated, in is replaced with a
randomly chosen patch from the dataset of all existing
patches.
Mutation Rate between 0 and 1:
All of our experiments used a mutation rate between 0.1 or
0.3.
<0.1 results in not enough variety in children.
>0.3 results in too much difference between parent and
child and frequent undesirable mutations.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

21. Interactive Evolution:
Parent Selection
User picks terrains that exhibit the features they
desire.
User chooses 0 – 2 parents.
No terrains other than those chosen by the user are
considered for breeding.
0 parents: All offspring are randomly generated.
1 parent: All offspring are mutated versions of the parent.
2 parents: All offspring are the result of crossover between
parents and individual mutations.
Elitist selection: User selected parents each have one
child that is an exact copy.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

22. Interactive Evolution:
Parent Selection
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

23. Interactive Evolution:
Gene Selection
User specifies which patches are immune to
crossover and mutation.
All selected patches will not change from the base
parent.
All unselected patches may experience crossover an
mutation as usual.
Inverse mutation rate:
More patches selected, higher chance of mutation for
unselected patches.
High chance of mutation when only a few patches
unselected.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

24. Interactive Evolution:
Gene Selection
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

25. Why use Gene Selection?
Reduce user fatigue!
In short:
Protect good patches from mutation.
Promote the change of bad patches.
Swap out these two patches and
keep everything else as is. Difficult
when only using Parent Selection.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

26. Results: Sample Run (1)
Generation 1:
Randomly generated.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

27. Results: Sample Run (2)
Generation 4:
Main feature starts to appear.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

28. Results: Sample Run (3)
Generation 6:
Prominent main feature but a few wrong patches.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

29. Results: Sample Run (4)
Four results from four separate runs
All with the same user goal
9 generations 7 generations
13 generations 16 generations
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

30. Results: Game Terrain (1)
Original map from the game Halo (left).
Terrain generated by our system (right).
Generated through multiple runs with different parameter settings
The resulting terrain from one run was used as a sample terrain to the next run.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

31. Results: Game Terrain (2)
Variations on the result terrain.
Small variations can result in new experiences for the
player without needing to learn an entirely new map.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

32. Future Challenges
Move towards automated fitness evaluation.
Retain some manner of Gene Selection.
Refine type of terrain being generated by
focusing on a single game genre.
Currently, we generate Virtual Terrain but we want
one system to generate complete Game Maps
Investigate player profiling to drive the
evolutionary process.
Allows for possibilities in tailoring game maps to
individual players.
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia

33. Questions?
GECCO’11, July 14th W. Raffe, F. Zambetta, X. Li
Dublin, Ireland
Evolving Patch-based Terrains RMIT University, Australia