EVO*-2011

Evolving Interesting Maps

for a First Person ShooterLuigi Cardamone, Georgios Yannakakis,Julian Togelius and Pier Luca Lanzi

Luigi Cardamone et al. – EVO* 2011

Motivations

Game content?

In many games, the availability of a big quantity of content is crucial

Unfortunately, the creation of the game content is a very time-consuming activity

Several game designers are required

Some content need to be tested

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Luigi Cardamone et al. – EVO* 2011

SBPCG

Search-based Procedural Content Generation (SBPCG):

PCG (algorithms procedures for generating content)+

Search algorithms (e.g. genetic algorithms)

SBPCG can be a valid tool:

To support the game designers

To extends the game with unlimited content

Challenges:

How to represent the content?

Which is the best representation to be evolved with genetic algorithms?

How to evaluate the game content?

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Luigi Cardamone et al. – EVO* 2011

This work

Focus on a First Person Shooter

We Apply SBPCG to evolve maps that can be interesting for the players

We focus on maps:

For Multi-player deathmatch

Single floor

We introduce 4 genotype representations for the maps

We propose a fitness to measure the interestingness of a given map

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Luigi Cardamone et al. – EVO* 2011

Cube-2 6

Sophisticated First Person Shooter Game:

Deathmatch

Team Deathmatch

Capture The Flag

Fast Engine allowing for in-game geometry editing

Open-source

Luigi Cardamone et al. – EVO* 2011

Problem Setting

Map elements:

Structural elements:

• Rooms

• Corridors

• Elevators/stairs

Game Elements:

• Weapons

• Bonus

• Spawn-points

Graphical Elements:

• Non-interactive objects

• Lights

• Textures

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Part of the representation

Algorithmically generated

Luigi Cardamone et al. – EVO* 2011

Representations

Phenotype:

Single floor map (2D)

A big matrix of blocks that canform corridors and rooms

4 Genotype Representations:

All-White

All-Black

Grid

Random Digger

Mapping procedures are necessary to convert the genotype to the phenotype:

Check unreachable parts

Place uniformly weapons, bonus, and spawn points

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Luigi Cardamone et al. – EVO* 2011

All-White

The initial space is free

The genome encodes a series of obstacles that are added to the map

Each obstacle is a wall represented as:

x,y (start position)

Length (positive for horizontal, negative for vertical)

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<(10,10,200),(90,90,-100),…>

Luigi Cardamone et al. – EVO* 2011

All-Black

The initial space is all black (full of obstacles)

The genome encodes a series of free spaces that are added to the map

Each free space can be:

A square arena (x,y,size)

A rectangular corridor (x,y,length)

Luigi Cardamone et al. – EVO* 2011

Grid

The initial space has a grid of walls that creates NxN squares

The genome specifies for each wall of the grid if it should be ON or OFF

110111110111

111011101111111111

111111

Luigi Cardamone et al. – EVO* 2011

Random Digger

The initial space is all black (full of obstacles)

An agent starts moving randomly and “dig” every place visited

The genome encodes the policy which controls this agent

Luigi Cardamone et al. – EVO* 2011

Removing Unreachable parts

All the first 3 representations may lead to maps with unreachable parts:

Remove the parts that are not reachable:

1. Compute the connected spaces

2. Find the biggest space

3. Remove the zones that are not reachable from the main one

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1

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Luigi Cardamone et al. – EVO* 2011

Fitness

Based on the Fighting Time

Also the size of the map is taken into account

Fitness = FightTime + Size

To evaluate the FightTime:

Simulated match of 10 minutes

4 bots (need navigation points)

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spawn start-fight killed spawn start-fight killed

Fighting Time Fighting Time

Results

Luigi Cardamone et al. – EVO* 2011

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Best Maps evolved with representation All-White

Best Maps evolved with representation All-Black

Luigi Cardamone et al. – EVO* 2011

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Best Maps evolved with representation Grid

Best Maps evolved with representation RandomDigger

Luigi Cardamone et al. – EVO* 2011

Comparison

10 runs for each representation

Grid and All-White are the representations which achieve the highest fitness

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> >

Luigi Cardamone et al. – EVO* 2011

Maps Loaded in Cube 2 19

Luigi Cardamone et al. – EVO* 2011

Game-play example 20

Luigi Cardamone et al. – EVO* 2011

Conclusions

All the representations are able to evolve playable maps

Preliminary user testing suggests that the fitness push towards interesting maps

All-White is the representation that is able to evolve the best maps

Future works:

Use additional fitness functions

Extends the analysis using more human players

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Thank you! Questions?