gecco 2011

Evolving Patch-

based Terrains for use in Video Games

William Raffe, Fabio Zambetta, Xiaodong LiRMIT University, Melbourne, Australia

Evolving Patch-based TerrainsGECCO’11, July 14th

Dublin, Ireland

W. Raffe, F. Zambetta, X. Li

RMIT University, Australia

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


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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


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Height-map Terrains


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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.


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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.


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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.


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Recombining Patches

(Roof-Tiling)

� Choose a starting patch


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Recombining Patches

(Roof-Tiling)


� Choose the next patch and overlap it with the one before it.


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Recombining Patches

(Roof-Tiling)



� Keep adding patches until an entire row is complete.


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Recombining Patches

(Roof-Tiling)




� Repeat this to create a second row.


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Recombining Patches

(Roof-Tiling)





� Overlap the second row with the first.


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Recombining Patches

(Roof-Tiling)





� 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.


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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.


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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.


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Patch and Overlap Size

Reduce

Overlap

Size

Increase

number of

patchesNumber of Patches = 4 (2x2)Overlap Size = 80%

Number of Patches = 81 (9x9)

Overlap Size = 20%


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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.

13 455

22

34 18

15 9

40

Terrain made up of 9 patches (3x3)


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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.


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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.


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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.


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Parent Selection


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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.


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Gene Selection


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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 andkeep everything else as is. Difficultwhen only using Parent Selection.


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Results: Sample Run (1)

� Generation 1: � Randomly generated.


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� Generation 4: � Main feature starts to appear.


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� Generation 6: � Prominent main feature but a few wrong patches.


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� Four results from four separate runs

� All with the same user goal

9 generations 7 generations

13 generations 16 generations


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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.


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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.


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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.


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Questions?

gecco 2011

Education