Evolving Interesting Maps for a First Person Shooter

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Evolving Interesting Maps
for a First Person Shooter

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Evolving Interesting Maps for a First Person Shooter

  1. 1. Evolving Interesting Mapsfor a First Person ShooterLuigi Cardamone, Georgios Yannakakis,Julian Togelius and Pier Luca Lanzi EVO*-2011
  2. 2. Motivations 2 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 Luigi Cardamone et al. – EVO* 2011
  3. 3. SBPCG 3 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? Luigi Cardamone et al. – EVO* 2011
  4. 4. This work 4 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 Luigi Cardamone et al. – EVO* 2011
  5. 5. 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
  6. 6. Problem Setting 7 Map elements: Structural elements: • Rooms Part of the • Corridors representation • Elevators/stairs Game Elements: • Weapons Algorithmically • Bonus generated • Spawn-points Graphical Elements: • Non-interactive objects • Lights • Textures Luigi Cardamone et al. – EVO* 2011
  7. 7. Representations 8 Phenotype: Single floor map (2D) A big matrix of blocks that can form 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 Luigi Cardamone et al. – EVO* 2011
  8. 8. All-White 9  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)<(10,10,200),(90,90,-100),…> Luigi Cardamone et al. – EVO* 2011
  9. 9. 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
  10. 10. 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 110111 110111 111011 101111 111111 111111 Luigi Cardamone et al. – EVO* 2011
  11. 11. 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
  12. 12. Removing Unreachable parts 13 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 1 3 2 Luigi Cardamone et al. – EVO* 2011
  13. 13. Fitness 14  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)spawn start-fight killed spawn start-fight killed Fighting Time Fighting Time Luigi Cardamone et al. – EVO* 2011
  14. 14. Results
  15. 15. 16Best Maps evolved with representation All-WhiteBest Maps evolved with representation All-Black Luigi Cardamone et al. – EVO* 2011
  16. 16. 17 Best Maps evolved with representation GridBest Maps evolved with representation RandomDigger Luigi Cardamone et al. – EVO* 2011
  17. 17. Comparison 18 10 runs for each representation Grid and All-White are the representations which achieve the highest fitness > > Luigi Cardamone et al. – EVO* 2011
  18. 18. Maps Loaded in Cube 2 19 Luigi Cardamone et al. – EVO* 2011
  19. 19. Game-play example 20 Luigi Cardamone et al. – EVO* 2011
  20. 20. Conclusions 21 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 Luigi Cardamone et al. – EVO* 2011
  21. 21. Thank you! Questions?

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