VDP2016 - Lecture 14 Procedural content generation
1 like•344 views
La generación de contenido procedural (PCG) implica la creación de niveles, mapas y otros elementos del juego de forma algorítmica con poca o ninguna intervención humana, utilizando conocimientos de dominio y técnicas de inteligencia artificial. Este proceso se basa en la aleatoriedad estructurada y la integración de varias capas de contenido para mejorar la jugabilidad. Se requiere una comprensión profunda del diseño de niveles para implementar PCG de manera efectiva y generar experiencias únicas para los jugadores.
VDP2016 - Lecture 14 Procedural content generation
- 1. Pier Luca Lanzi Procedural Content Generation Pier Luca Lanzi, Daniele Loiacono, Michele Pirovano
- 2. Pier Luca Lanzi What is Procedural Content Generation? Procedural Generation with no or limited human intervention, algorithmically of Content of “things that affect the gameplay”, not non-player character behavior, not the game engine
- 3. Pier Luca Lanzi so what exactly is procedurally generated? levels, tracks, maps, terrains, dungeons, puzzles, buildings, trees, grass, fire, plots, descriptions, scenarios, dialogues, quests, characters, rules, boards, parameters, camera viewpoint, dynamics, weapons, clothing, vehicles, personalities, etc.
- 6. Pier Luca Lanzi BBC Micro – 2 MHz MOS Technology 6502/6512 16-128 kB RAM 32-128 kB ROM
- 10. Pier Luca Lanzi What the ingredients? domain knowledge artificial intelligence structured randomness multi-layering filters, limits & restrictions specialized algorithms gameplay integration
- 11. Pier Luca Lanzi Ingredients #1 & #2 Domain Knowledge & Artificial Intelligence • Domain Knowledge § To generate something you need to know it § PCG typically aims at building an artificial level designer, usually needs domain knowledge about level design • Artificial Intelligence § Need algorithms that can work on complex knowledge and generate plausible content § Search-based methods, L-systems, evolutionary computation, fractals, cellular automata, agent-based methods, planning, etc. 11
- 12. Pier Luca Lanzi
- 13. Pier Luca Lanzi ingredient #3 structured randomness things look like they have been randomly generated but it is not completely at random!
- 14. Pier Luca Lanzi f(x) = sin(x)
- 15. Pier Luca Lanzi
- 16. Pier Luca Lanzi they both look like “noise” but one of them feels like it has structure… it is structured randomness
- 17. Pier Luca Lanzi
- 18. Pier Luca Lanzi Example: Building Mario Levels • Library of chuncks. • Irregular in size. • Mixed purpose: §Mainly geometry. §Mainly gameplay. • Anchor points (Mario) 18
- 19. Pier Luca Lanzi Algorithm • Chunk Filtering §Some chunks just don‘t match the current level. • Chunk Selection §Various metrics, randomness and designer settings. • Chunk Insertion §Remove overlapping components. • Post-Processing §Cosmetic and gameplay adjustments. 19
- 20. Pier Luca Lanzi Infinite Mario Level 20
- 21. Pier Luca Lanzi Map & Cave Generators • Cave generators typically are based on Cellular Automata • Map generators typically an approach §Tile-Based §Digger §Uniform 21
- 22. Pier Luca Lanzi Tile-Based Map Generation • Simple to implement • Requires rich tileset or it can easily lead to repetitions 22
- 23. Pier Luca Lanzi Uniform Map Generators • Two steps §First create rooms based on a high level grid §Next, create the corridors to connect the rooms • Rather simple to implement • Limited topology by the room shape and the grid 23 Rogue (1980)
- 24. Pier Luca Lanzi Digger Generators • Simulates the behavior of a mole that digs its den • Generates maps that have a tree like structure 24
- 25. Pier Luca Lanzi ingredient #4 multi-layering typically more layers of procedural content generation are applied in sequence
- 26. Pier Luca Lanzi • Warzone 2100 §Heights & Cliffs §Roads §Textures §Player Bases §Local Features • Civilization 4 §Fractal Heightfield §Plate Tectonics §Tile Types §Rivers and Lakes §Map Bonuses
- 27. Pier Luca Lanzi ingredient #5 Filters, Limits & Restrictions “In Civ3 I would say we even shipped with a sub-standard resource placement algorithm where all the iron could be concentrated in just a few small locations on the map, and for one player there may be literally no way for them to build swordsmen.” – Soren Johnson "With Civ4 we instituted randomness with limitations. There always has to be a minimum distance between each element of iron, or each element of horses, and that completely solved the problem.” – Soren Johnson
- 28. Pier Luca Lanzi ingredient #6 specialized algorithms placing special items, requires special tricks this tricks must be encoded in the PCG
- 29. Pier Luca Lanzi 1. Connect all bases, the resources, pick three random points and connect them 2. Apply a customize A* heuristic and reuse roads! 3. Customize A* heuristic with randomize cost of non-road grid cells.
- 30. Pier Luca Lanzi ingredient #7 gameplay integration Is it fun to play? Is the progression adequate?
- 31. Pier Luca Lanzi
- 32. Pier Luca Lanzi is this all there is?
- 33. Pier Luca Lanzi PCG Is it done online? Or offline? Is it necessary content? Or optional? Do you use random seeds or parameter vectors? Is it stochastic? Or deterministic? Generate and test? Constructive?
- 34. Pier Luca Lanzi Domain Knowledge • To build an artificial intelligence, you typically need a lot of domain knowleddge • When using PCG you‘re building an artificial level designer, so you usually need domain knowledge about level design • So you‘ll probably need a real level designer and people who have some experience • When you build a particular level you don‘t need to understand the general concepts behind the game mechanics as much as when you do it procedurally • When you go procedurally, you have to understand all the space of options you are creating and understand how your game mechanics tie in with the level • Provide ways to customize your algorithms and tools for controlling and visualizing the result 34
- 35. Pier Luca Lanzi Inputs & Senses • Procedural generation algorithms work on different sets of inputs and actions (when compared to traditional AI) • Examples of Inputs § Line of Sight § Point Distance (A*, Dijkstra) § Obstacles & Clearance § Height • Examples of Actions § Place resources § Layout buildings § Create roads/rivers § Adjust terrain 35
- 36. Pier Luca Lanzi Frozen Synapse
- 37. Pier Luca Lanzi Level Generator #1 37
- 38. Pier Luca Lanzi Human Designed Levels 38
- 39. Pier Luca Lanzi Algorithm 1. 2. 3. 4. 39
- 40. Pier Luca Lanzi Step 5. 40
- 41. Pier Luca Lanzi Step 6. Walls & Doors 41
- 42. Pier Luca Lanzi Step 7. Windows & More Doors 42
- 43. Pier Luca Lanzi Step 8. Box Cover 43
- 44. Pier Luca Lanzi Example #1 44
- 45. Pier Luca Lanzi Example #2 45
- 46. Pier Luca Lanzi Example #3 46
- 47. Pier Luca Lanzi Example #4 47
- 48. Pier Luca Lanzi No Man’s Sky from nucl.ai conference
- 49. Pier Luca Lanzi Philosophy • Artist directed • Procedural generation is irrelevant to end user • Motivation §Game world scale §Output from small team §Unexpected outputs §Individual experiences • Engine agnostic to content origin • Mix of general and specific techniques 49
- 50. Pier Luca Lanzi Multi-Layered Generation 50
- 51. Pier Luca Lanzi Region Creation Process 1. Generation 2. Polygonisation 3. Spherification 4. Physics construction 5. AI knowledge construction 6. Decoration 51
- 52. Pier Luca Lanzi Generation Requirements • Directable and consistent • Real-time • Varied • Real world and abstract shapes • Easy to modify and add to • Data local 52
- 53. Pier Luca Lanzi Perlin/Simplex Noise • Well known techniques §Widely applicable §Scalable §Repetitive §Insufficient • Modified version §Perlin worms §Noise with a threshold §Paths, caves, lines 53
- 54. Pier Luca Lanzi Structured Shapes • Cellular noise patterns • Worley/Voronoi Noise • Pickpoints • Calculate min distances • Cliffs, paths, regions... 54
- 55. Pier Luca Lanzi Mathematical Solids • Gielis, Johan (2003), "A generic geometric transformation that unifies a wide range of natural and abstract shapes", American Journal of Botany • Natural looking shape additions 55
- 56. Pier Luca Lanzi Map Generation in In Verbis Virtus (Mattia Ferrari) 56
- 57. Pier Luca Lanzi Map Generation for In Verbis Virtus • Requirements §Control over the complexity of the generated maps §Continuous representation §Avoid decors overlaps • Combination of diggers and uniform approaches • Map elements from human designed levels §Walls §Doors §Decors 57
- 58. Pier Luca Lanzi Step 1: Growth • Similar to the digger approach • Builds a map structured as a tree • Growth is controlled by a set of parameters, including, §Type of elements §Number of rooms §Number of branches §Available area 58
- 59. Pier Luca Lanzi Step 2 & 3: Additional Corridors and Deadline Elimination • Additional Corridors §Similar to uniform generators §Based on the required percentage of connections to create §Distance between areas • Deadline Elimination §Based on the percentage of dead ends to eliminate 59
- 60. Pier Luca Lanzi Map Evaluation • Given a map we needed to compute several statistics to filter the most interesting ones • Maps are encoded as graphs • Compute several metrics §Max distance among rooms §Reachability §Flow §… 60
- 61. Pier Luca Lanzi Some Findings • 75% or more additional connections increase the complexity of the map without improving complica le mappe senza migliorare la raggiungibilità • The generator is robust with respect to the room shape • By eliminating the start room, we can reduce the reachability over the entire map
- 62. Pier Luca Lanzi Mappa generata
- 63. Pier Luca Lanzi Video finale
- 64. Pier Luca Lanzi we can do it, so can you!