Re-definition of the generator презентация
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- 2. Goals for a Generator We have defined the process mechanically, but
- 3. Goals for a Generator Speed Ability to have content not slow
- 4. Goals for a Generator Expressivity & Diversity The generated assets should
- 5. Taxonomy Togelius et al. set a Taxonomy on how a generation
- 6. Taxonomy Online v. Offline Location of the generation on the host
- 7. Taxonomy Generic v. Adaptive Does it adapt to the player’s actions
- 8. Example Taxa Faster than Light
- 11. Quick And Dirty Summary Class is biomodal in their time playing
- 12. Why This Course Interested/Interesting Perspective area of employment Understand why this
- 13. Return Implementation of skills Examples Advantages and Disadvantages
- 14. Portfolio 2 Elements @ 20% each 4 Categories Implement a research
- 15. Textures and Landscapes
- 17. Spectrum of Need Flight Simulation
- 18. Spectrum of Need FPS
- 19. Representation Intensity and Height Maps Grid of Values
- 20. Pure Randomness For each square – take a number from a
- 21. Forced to be Smooth -Interpolation Real terrain is not random -
- 23. Lattice Size Larger Lattice – Places large peaks and valleys Smaller
- 24. Bilinear Weighted Average coming from the value of both points, interpolate
- 25. Also Works for Textures This method was originally applied to the
- 26. BiCubic Linear is perhaps not as realistic, we want a smooth
- 27. BiCubic Example x=1 10% distance of the slope face S(.1) =
- 28. Moving Past Direct Generators Idea till now have been to generate
- 29. Agent Generation
- 30. Doran and ParBerry 2010 Use the definition of an agent as:
- 31. Initial State Flat World bellow a waterline Representation as a height
- 32. Coastline Agent Starts with a seed location, a preferred direction (repulsor,
- 33. Coastline Agent When done subdivision will begin to spend tokens Selects
- 34. Smoothing Agent Makes a random walk about the space and changes
- 35. Beach Agents Search the area on the coastline and reduce their
- 37. Mountain Agents Starts at a random location and has a preferred
- 38. Hill Agents Similar to Mountain agents Expend more tokens for each
- 39. River Agents Begin on the coastline Will walk from the coastline
- 41. Ashlock and McGuiness 2013 Evolutionary Approach to Landscapes Matches a Target
- 42. Method Recursive quartering of the map space Automation holds a state
- 43. Automation Representation is an automation of transitions and actions Actions are
- 44. EA System Create a randomized population made up of chromosomes, data
- 45. Crossover in a GA on Strings
- 46. Crossover and Mutation Operations Treats the structure as linear about states
- 47. Evaluation Against a Target Target is the ideal function For each
- 49. Beyond Squares Hex based Games instead of squares Height map values
- 50. Civ 5?
- 51. Factals
- 52. FracTals Self Similar Mathematical Structures Recursive Definitions Create structures which have
- 53. Koch Curve Each line has a triangle placed in the centre
- 54. Midpoint Displacement Fractal Midpoint_Displacement(Startx,Endx,Run) Select the midpoint Midx<-Startx + (Endx
- 56. Diamond Square Algorithm Fournier et al. (1982), purposed for terrain by
- 57. Diamond-SQuare Average over the 4 points (3 if on edge) Add
- 58. DSFactal DSFractal(startx,starty,endx,endy,run) midx<-startx+(endx-startx)/2 midy<-starty+(endy-starty)/2 Square(startx,starty,endx,endy,midx,midy) Diamonds(startx,starty,endx,endy,midx,midy) DSFactal(startx,starty,midx,midy) DSFactal(midx,starty,endx,midy) DSFactal(startx,midy,midx,endy) DSFactal(midx,midy,endx,endy)
- 60. Torus x Mod Xn y Mod Yn Note in C/C++ that
- 61. Brown et al. (2011) Fractal Photomosaic images Idea – to
- 63. Mosacs Coloured Shards of Pottery, Stone, or Glass plastered to walls
- 64. Photo Mosaics First algorithmic version by R. Silvers Holds Patent
- 65. Making a Photo Mosaic Break Images into Blocks Take a set
- 66. Lenna Image Scanned in from a Playboy Centerfold Lena Söderberg in
- 69. Mandelbrot Set Defined on the complex number set A point in
- 71. Data Structure X,Y values of the location of the start of
- 72. Colouring Colouring based on the escape value of the factal Colouring
- 73. Search Through Fractals Genetic Algorithm Image Structure in box and Colour
- 74. Expanding on These Techniques Target v. generation Animation of the process?
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