Category: Procedural Generation

Procedural Generation – Far Cry 5 GDC Content Generation

digm520admin / / GDC, Procedural Generation, Talks
July 30th, 2018

Procedural Generation – GDC 2018 Talk – Procedural World Generation of ‘Far Cry 5’

By: Etienne Carrier

GDC 2018 – Procedural World Generation of ‘Far Cry 5’ – GDC Vault Link

GDC talk where Etienne details a large scale tool they created using procedural generation techniques to help build the world of Far Cry 5. It covers both the user end (world editors/designers) and the behind the scenes processes that make everything possible. They use Dunia and Houdini together to create these tools.

digm520admin / / Evolutionary Algorithms, GameDesign, Procedural Generation, Research
May 22nd, 2018

Research Papers on Procedural Content Generation for Games

Darwin’s Avatars: A Novel Combination of Gameplay and Procedural Content Generation

by: Dan Lessin, Sebastian Risi

Citation: [Lessin & Risi, 2015] D. Lessin and S. Risi, “Darwin’s Avatars: A Novel Combination of Gameplay and Procedural Content Generation,” 2015, pp. 329–336.

Key Terms:

  • Evolved Virtual Creatures
  • Artificial Life
  • Muscles Drives
  • Physics-based Character Animation
  • Procedural Content Generation
Summary

Use evolved virtual creature (EVC) system from Lessin et al. reference to procedurally generate creatures that provided interesting control and locomotion problems. Originally these were piloted by an AI trying to learn how to move, but this “brain” was removed for this paper so that players could run them. Movement was determined by the activation of muscles/actuators between the procedurally generated segments of the creatures. These muscles could range in value from [0, 1], where 0 is fully relaxed and 1 is fully activated. Players would press keys to activate muscles in an attempt to move the creature.

The morphology of the creature was made up of PhysX primitive (cubes, spheres, and capsules). Body in original paper was coevolved with the locomotion system to help ensure that the creature had successful ways to move. The gameplay was compared to QWOP and Incredipede

The conclusions were: novel combination of gameplay and procedural content generation made possible by evolutionary computation, a new way of unique creature control with 3D creatures not created by the user, and game can generate novel control challenges on its own.

Further Sources to Look Into from This:
  • [8] M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, USA, 1998.
  • [15] N. Shaker, G. N. Yannakakis, J. Togelius, M. Nicolau, and M. O’Neill. Evolving personalized content for Super Mario Bros using grammatical evolution. In Proceedings of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE 2012), Menlo Park, CA, 2012. AAAI Press.

Petalz: Search-Based Procedural Content Generation for the Casual Gamer

by: Sebastian Risi, Joel Lehman, David B. D’Ambrosio, Ryan Hall, and Kenneth O. Stanley

Citation: [Risi et al., 2016] S. Risi, J. Lehman, D. B. D’Ambrosio, R. Hall, and K. O. Stanley, “Petalz: Search-Based Procedural Content Generation for the Casual Gamer,” IEEE Transactions on Computational Intelligence and AI in Games, vol. 8, no. 3, pp. 244–255, Sep. 2016.

Key Terms:

  • Collection Mechanics
  • Compositional Pattern Producing Networks (CPPNs)
  • Procedural Content Generation
  • 3-D Printing
Summary

This research explored the use of PCG for a casual, social, collection mechanic focused game. In this research it also explored the effects of PCG on markets for in game economies and translation to real world objects with 3D printing.

The game uses procedural generation along with AI to create an Interactive Evolutionary Computation (IEC) system to create the flowers in the game. The generator is a modified compositional pattern producing network (CPPN) and the evolutionary algorithm chosen was Neruoevolution of Augmenting Topologies (NEAT).

The game focuses on users breeding flowers which can be seen by other players. These other players can “like” their flowers or purchase ones that are put up for sale. Breeding is done by three methods: pollination (allow for natural mutation through a single flower), cross pollination (combine traits of two different flowers), and cloning (simply replicate a flower). The player’s selection of what and how to breed is the interactive part of this IEC system, which allows the user to employ a search-based method of exploring the PCG design space.

To help provide a goal for players, this research also looked into creating a guided categorization system using the inherent properties of the parametric PCG technique used to create these flowers. They took advantage of the fact that this technique can lead to spatial design spaces where objects near each other in the design space will also tend to have similar aesthetic attributes. They explore this further with a Self-Organizing Map (SOM).

digm520admin / / Cellular Automata, Procedural Generation, Programming, Research, Scripting, Unity
May 20th, 2018

Sources to Learn Procedural Generation Techniques for Games

Procedural Generation in Unity

Unity Tutorial: A Procedurally-Generated Galaxy – Part 1 by: quill18creates

Youtube Link to Part 1 of Tutorial

A tutorial to look into for basic procedural generation of a galaxy in Unity. Also has some information on different variable types in C#.

Unity – Cellular Automata Tutorial

Unity Link to Cellular Automata Tutorial

Tutorial directly from unity on the basics of using the procedural generation technique of cellular automata.

digm520admin / / AI, cgNEAT, CPPN, Evolutionary Algorithms, GameDesign, Procedural Generation, Research
May 20th, 2018

Procedural Content Generation in Games

Galactic Arms Race (GAR)

Youtube Link – Evolving Particle Weapons in Galactic Arms Race | AI and Games

by: AI and Games

AI and Games briefly describes how GAR uses evolutionary algorithms to design weapons that the player likes using. The player takes the place of the fitness function to determine what is “good”. Techniques such as CPPN and cgNEAT are lightly touched upon and offer topics to be further explored.

digm520admin / / Math, Mesh, Procedural Generation, Readings, Research, Unity
May 13th, 2018

Procedural Generation

Unite 2016 – The Power of Procedural Meshes

Unity Talk by Alexander Birke

Youtube Link to Talk

Suggested Reading:

“Essential Mathematics for Games – A Programmers Guide” by James M. Van Verth & Lars M. Bishop

Links to examples to learn about procedural generation of meshes
Notes from talk:
    Why use procedural meshes?

  • Player Made Content (Ex. Spore)
  • Unique Mechanics (Ex. Gish)
  • Procedural Generation (Ex. Sir You’re Being Hunted)
Unity Procedural Meshes
  • Mesh starts by creating vertices, and then making triangles from those vertices
  • Components Needed:
    • MeshFilter – stores the mesh
    • MeshRenderer – shows the mesh
    • Script – to generate the mesh
  • Normal vectors used to determine surface orientation
  • Colors
    • Used to be determined by assigning values to vertices
    • Can be used to provide more information to vertices still
    • Can be done as bytes or floats
    • Bytes generally better
  • UVs
    • 2d coordinates for each vertex
    • Used to map textures
    • Create vector2d array to assign to mesh object
  • Setting Trangle Indices
    • Most difficult part
    • Find common features of topology that repeats
    • Draw it out to help find these patterns!
  • Delaunay Triangulation
    • Maximizes angle of all angles of triangles in triangulation
    • Good for GPU rendering since skinny triangles can lead to visual artifacts
    • Port of triangle.net can be found in an above link
  • Debugging Procedural Meshes
    • Very important to do
    • Use Gizmos and Debug classes
    • Rotate camera to check if tris are facing correct direction
    • Turn on wireframe rendering
  • Optimizing Meshes
    • StaticBatchingUtility: batches multiple game objects together into fewer drawcalls but where each can still be culled
    • Mesh.CombineMeshes: Puts many meshes into one, good if individual meshes not likely to cull, but can break things if not done carefully
  • Optimizing Dynamic Meshes
    • Mesh.MarkDynamic: allocates a memory buffer of sorts to designated mesh to let graphics API know mesh data will change often
    • Base data structure to store data in is flat arrays when possible
    • Lists can be used too, especially if you are unsure how many vertices you will need before creating mesh
    • Frustrum Culling
    • Skinned Meshes
  • Multithreaded Mesh Generation: Unity API not thread safe
  • Compute Shaders
    • GPU great for running parallel tasks
    • Only available on modern platforms: PS4, Xbone, DirectX 11, OpenGL 4.3, OpenGL ES 3.1
    • Unity can cross compile with these options
Summary

Procedurally generating a mesh starts with creating vertices, and then triangles from these vertices. Normals and UVs can be added to this data for more informed meshes. In Unity, your mesh will need a MeshFilter and MeshRenderer component. Debugging your mesh is important, and some tools in Unity to help with that are the gizmo and debug classes, moving the camera around, and turning on wireframe rendering. There are also several options to look into to either optimize your existing meshes how they are, or compute them more efficiently.

More to Research
  • Bezier Curves: parametric mathematical curves used commonly in generating computer graphics
  • Use of GPU to do parallel calculations, and how to connect that with Unity scripting
  • Thread and Multithread Mesh Generation: Not sure what this actually meant
  • Computing shaders
  • Talk through Unity by “Sir You’re Being Hunted” on 3D Mesh Generation
digm520admin / / AI, GameDesign, GDC, Procedural Generation
May 7th, 2018

AI Wish List from GDC

AI Wish List: What Do Designers Want out of AI? – GDC Vault

GDC Talk with Raph Koster, Dave Mark, Richard Lemarchand, Laralyn McWilliams, Noah Falstein, and Robin Hunicke. They go over what directions they would like AI to move in in the future to further good game development.

Some Topics Discussed:
  • Content Creation: Don’t focus on procedurally generating a tree, or a forest of trees. Generate interestingly varied trees that have other interactive elements within/about them to create a much more detail-rich environment that could not be created by human hands in any practical way. Create a world, with a forest, with a pond, with tadpoles that you can interact with.
  • Feelings to Invoke: Feeling Proud for something. Have the player feel proud that they were able to guide something else to perform an action on its own.
  • Create systems of interactivity that decentralize the player. Make the player understand that their actions are upsetting a balance. Make the player see that their actions affect other lives, even NPC lives.
    Example: A player arrives at a procedurally generated world with procedurally generated pink moles that have built up an entire ecosystem and have a history. These moles will then react to the player arriving and their actions/decisions, but also carry on with their own lives, which they would have done regardless of whether a player ever arrived or not.
digm520admin / / GameDesign, Procedural Generation, Research, Talks
April 23rd, 2018

Using Procedural Generation Techniques in Game Design Effectively

GDC Talk – Math for Game Programmers: Semi-Procedural Content Pipelines – Squirrel Eiserloh

Link to GDC Vault (May be locked content)

2nd of 6 talks on semi-procedural content generation for games by Squirrel Eiserloh at GDC 2018. In this video he goes over many techniques and how to use them effectively.

  1. Variants:

    Have multiple versions of things. Multiple colors for grass, dirt tiles. Multiple different sounds when running through grass.
    Do I need a tile to be the same forever?
    Whenever possible, let the designer provide multiple alternatives.

  2. Blueprint Definitions:

    Don’t make an orc, create “orcness”. This blueprint has many ranges of values for different characteristics of a character.
    “Do I need an int, or an int range? Do I need a float or a float range?” Use these types of questions for every trait/parameter.
    Whenever possible, let the designer provide number ranges.

  3. Procedural Detailing:

    I paint important parts, algorithms fill in tedious boring labor.
    Example: Unreal fills in grass where designer says to put grass.
    Whenever possible, let the algorithm do the dirty work.

  4. Procedural Brainstorming:

    Use procedural generation to spark creativity.
    Whenever possible, let the algorithm spark your creativity.

  5. Content Injection:

    Inject hand crafted content into procedurally generated content.
    Whenever possible, let the designer inject handmade content into the procedural pipeline.

  6. Stitching:

    Create ways for your things to go together.
    Example: Speleunky – Has many map grid templates with connection points, so they connect in a sensible way.

  7. Template Instantiation:

    Load various copies of things into memory given space allowed.
    Ex: Load instances of rotations or variances into memory.

  8. Content Lists:

    Be a data whore.
    Have huge lists of data to use as names for things. Let content be pulled from these lists.

  9. Mad-Libs:

  10. Abstract Compositions:

    Allow designer to paint out abstract designs, and procedurally generate based on that.
    Ex: Lay out city design with 3 colors depicting residential, commercial, and industrial areas which can then be filled in appropriately.

  11. Constraints

    Say what you want the content to have.
    Ties in well with procedural recipes.
    Creates limits and ranges.

  12. Nested Constraints:

    Be consistent with terminology so that data can string together nicely from large encompassing objects/ideas down to the simplest singular objects that are generated.

  13. Exemplars:

    Human creates “good” content, then algorithm can:
    Make more like this (ex. Markov chains)
    Fill in the missing bits (ex. Wave Function Collapse)

  14. Training:

    Inject human designs into ML processes.
    Genetic algorithms (make a thing, rate it, then do more stuff, keep the best ones)

  15. Outputs as Inputs:

    Anything you generate in general can be an influencer in another thing that is generated.
    Generated content helps create more generated content.