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Procedural Content Generation

Procedural Content Generation. James Grisetti. Overview. Introduction Brief History of Procedural Generation Basic Generators Contemporary Generators Future Work Example Applications. Introduction. What is Procedural Content Generation?

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Procedural Content Generation

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  1. Procedural Content Generation James Grisetti

  2. Overview • Introduction • Brief History of Procedural Generation • Basic Generators • Contemporary Generators • Future Work • Example Applications

  3. Introduction What is Procedural Content Generation? • Generic term for the usage of algorithms to generate content rather than manual creation of content. • “Content” refers to media content such as in movies, games, pictures, simulation, etc.

  4. Introduction Why Use Procedural Generation? • Reduce production time by focusing on mechanics, rather than simulation artifacts • Maintain consistent design style across multiple artifact designs, while allowing for controlled variation

  5. Overview • Introduction • Brief History of Procedural Generation • Basic Generators • Contemporary Generators • Future Work • Example Applications

  6. (Very) Brief History • Procedural Content Generation has existed to some extent for as long as the Computer Science field • Simple generators have been used in Monte Carlo simulation • Used in computer games as early as 1982 • First notable use of procedural animation was in the Lord of the Rings trilogy • Widespread use in video games and simulation

  7. Overview • Introduction • Brief History of Procedural Generation • Basic Generators • Contemporary Generators • Future Work • Example Applications

  8. Basic GeneratorsLinear Congruential Generator • In the form of XN+1 = aXN + c (mod m) • Programmer controls output through values of a, c, m, and X0 (seed value) • Implemented in the C language through rand() and srand() • May be expanded to higher order polynomial

  9. Basic GeneratorsLinear Feedback Shift Registers • Based on a “shift register” with bits shifted in to the left and shifted out to the right • Programmer controls output through setting the “polynomial” and initial state • Slightly higher quality than LCGs

  10. Basic GeneratorsLinear Feedback Shift Register Varieties Fibonacci Galois Several bits are modified based on bit shifting out • Bit shifted in is based on several bits of current state

  11. Overview • Introduction • Brief History of Procedural Generation • Basic Generators • Contemporary Generators • Future Work • Example Applications

  12. Contemporary GeneratorsPerlin Noise • Mathematical interpolation of floating point numbers • Creates fractional Brownian Motion • Used to make CGI textures look more realistic

  13. Contemporary GeneratorsL-Systems • Lindenmayer systems • Defined as a 3-tuple V, the alphabet of the system (variables and constants) ω, initial string of the system (axiom, start, or initiator) P, set of production rules (one for each variable in V)

  14. Contemporary GeneratorsL-Systems Continued • At each iteration, each variable in the current string is replaced with its corresponding production to produce the next string • State is the string after a given number of iterations • The state is usually used to direct a “turtle graphic” • One of the most commonly used PCGs

  15. Contemporary GeneratorsL-Systems Continued Example V = {A,B} ω = A P = {A→AB, B →A} Iteration • A • AB • ABA • ABAAB • ABAABABA

  16. Contemporary GeneratorsL-Systems Demonstration 1 V = {F,+,-} F means “draw forward” ‘+’ and ‘-’ are used to change angle ω = F--F--F-- P = {F→F+F--F+F}

  17. Contemporary GeneratorsL-Systems Demonstration 2 V = {X,F,[,],+,-} F means “draw forward” ‘[‘ and ‘]’ are used to save and restore position ‘+’ and ‘-’ are used to change angle X has no meaning outside of the L-System ω = X P = { X → F-[[X]+X]+F[+FX]-X, F → FF }

  18. Contemporary GeneratorsExpert Systems • Departure from previous approaches • Use statistical methods to determine world shape and item placement • First notably used in Procedural Content Generation in Valve’s Left4Dead

  19. Overview • Introduction • Brief History of Procedural Generation • Basic Generators • Contemporary Generators • Future Work • Example Applications

  20. Future Work • Most contemporary PCGs are involved in generating terrain artifacts (landscape) • Some use in animations, textures, and lighting The next generation of Procedural Content Generation will be involved in creating entire maps

  21. Future Work Going to the extreme: Programming programs that program programs In a paper published this year, Mahlmann, Togelius, Yannakakis at the IT University of Denmark suggest the use of Procedural Content Generation for entire games, including rule sets, unit hierarchies, game maps, etc.

  22. Future Work Another paper by Dimovska, Jarnfelt, Selvig, Yannakakis from the same university explores the use of procedural techniques to aid in physical rehabilitation using the Wii platform

  23. Example ApplicationsMinecraft • Terrain is generated procedurally • “Mobs” are placed procedurally

  24. Example ApplicationsCityEngine • Middleware intended for simulation and use in motion pictures • Requires map data for input • Creates road layouts, building shapes, and textures based on user parameters

  25. Example ApplicationsSubversion • Independently developed game • Goal is for the entire world to be procedurally generated

  26. Example Applications.kkrieger • Created circa 2004 for the then state-of-the-art hardware • Uses PCG techniques to create maps, play music, and place items and enemies all at runtime • Executable is 97,280 bytes

  27. References Knowledge • The omniscient www.wikipedia.org • Mahlmann, et al. “Towards Procedural Strategy Game Generation: Evolving Complementary Unit Types” (link) • Dimovska, et al. “Towards Procedural Level Generation for Rehabilitation” (link) • Müller and Parish. “Procedural Modeling of Cities” SIGGRAPH 2001 (link) Example Applications: • http://www.theprodukkt.com/kkrieger • http://www.introversion.co.uk/subversion • http://www.procedural.com • http://www.minecraft.net

  28. "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." -- John von Neumann

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