Evolution of Artificial Terrains for Video Games Based on Accessibility

Miguel Frade – Instituto Politécnico de Leiria, Portugal F. Fernandez de Vega – University of Extremadura, Spain Carlos Cotta – University of Málaga, Spain

Istanbul, Turkey 7 – 9 April 2010

Introduction(1) ●

There are a wide range of techniques for terrain generation ●

procedural techniques, like fractals are common

But how to control terrains features?

Introduction (2) ●

With constraints ●

computing the interpolation of point clouds or contour lines down-sampled elevation data sets using radial basis functions patches, small-scale or microscopic features of existing terrains to synthesize new ones deformation of initial fractal terrain by applying local modifications constrained fractal model

Introduction (3) ●

Limitations of current constraints approaches ●

are focused on the reconstruction of Data Elevation Models most suffer from either time and/or manipulation complexity use only height constraints ●

both the height and location of constraints must be specified this kind of constraints are too strict

Introduction (4) ●

There are situations were less strict restrictions are preferable, like: ●

a nearly flat, or mountainous terrain

without specific size or location

Approaches with less strict constraints ●

Technique proposed by J. Olsen (2004) ●

May not fully meet the constraints

Our previous work: GTPi ● ●

Interactive Evolutionary Computation Might be tiresome

Introduction (5) ●

Our Goal –

automatically evolve terrains with GP based on accessibility constraints

Why accessibility? ●

highly influences the attainable movements of characters/avatars as well as placement of buildings and other structures easily evaluated by computers ●

Accessible if slope < slope threshold

Evaluation Algorithm 1) Generate height map H from TP 2) From H calculate slope map S 3) From S calculate accessibility map A 4) Determine largest connected area on A 5) Calculate accessibility score – fitness value

1) Generate height map H from TP TP = cos(atan(minus(exp(X),myDivide(multiply(0.07358, 0.93756), myDivide(X,myLog(myLog(sin(Y)))))))) Dx


Terrain area at 100%

Dx/ S x

View area




Dy Dy Sy


2) Calculate slope map S from H

3) Calculate accessibility map A from S

4) Largest connected area on A ●

Component labeling algorithm

Smaller areas are removed

Ca = number of cells of largest accessible area

5) Calculate accessibility score nr n c = , Ca

C a≠0

nr nc t = , ⌈ p nr nc ⌉


70 % Test values p= 80 % 90 %

0 p1

 s=∣− t∣

Tests Parameters

T 1= { noise  x , y  , erc } T 2 ={ x , y ,erc } T 3 ={ noise x , y  , x , y , erc }

3 terminal sets, 3 pa values

Total 9 tests

GP Function Set

Results (1) – T1 ●

present several distributed obstacles smooth transitions terrains tend to be similar

Results (2) – T2 ●

Many diverse terrains geometric patterns and sudden height changes tend to present a single large obstacle

Results (3) – T3 ●

some geometric patterns Some natural looking terrains Some odd looking terrains

Results (4) ●

Subjective classification of terminal sets –

Realism ●

Diversity ●

T3, T1, T2 T3, T2, T1

Suitability ●

T1, T3, T2

Results (5) ●

GP run data

Results (6) ●

All runs reached the goal of fitness = zero

How different are the resulting terrains? –

Measure accessibility maps overlap

Results (7) ●

Accessibility maps over lap

Conclusions (1) ●

Fitness function is able to find many solutions

The number of generations is little influenced by

changing the terminal set, or

the amount of desired accessible area (p)

The terminal set has a great influence on the terrain look Rich terminal sets allow shorter TPs

Conclusions (2) ●

Our fitness function does not account for terrain realism –

requiring a visual inspection before using them

But less visualizations than with IEC systems

minimizes designer fatigue

Sometimes terrains present a single or a few large obstacles

Conclusions (3) ●

Technique in use in Open Source video game Chapas http://sn.im/chapas

Play demo video

Thank you!

Any questions ?

Evolution of Artificial Terrains for Video Games Based ...

With constraints. ○ computing the interpolation of point clouds or contour lines .... Fitness function is able to find many solutions. ○ The number of generations is ...

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