Category: GameDesign

GDC Talks – GDC 2018 – Math for Programmers by Squirrel Eiserloh

digm520admin / / GameDesign, GDC, Procedural Generation, Squirrel Eiserloh
September 26th, 2018

Math for Programmers: 6 Courses on Procedural Content Generation for Games – GDC 2018

by: Squirrel Eiserloh

There are 6 talks about techniques and implementations of procedural content generation in games. The order is as follows:

  1. The Power of Procedural Recipes – Procedural Recipes
  2. Semi-Procedural Content Pipelines – Semi-Procedural Methods
  3. Staged Parametric Map Generation – Staged Parametric Generation
  4. Digging with Perlin Worms – Perlin Worms (for rivers/roads/caves)
  5. Discrete Constructs in Endless Worlds – Infinite Worlds
  6. Juicing World Generation with Metadata Feedback – Juicing PCG with Metadata

DIGM 540 – Engineering Statics Game Concept 00 – Ring with Strings

digm520admin / / DIGM540, engineering, Game Work, GameDesign, StaticsGame, Unity
May 24th, 2018

Engineering Statics Game Concept – 00

Vimeo – Concept for Engineering Statics Game

This was a quick proof of concept to show the idea from the last post at work. This concept takes inspiration from the hands on engineering kit that used masses on strings tugging on a central ring object to teach topics such as center of mass and vector math.

The forces from the nodes on the ring are calculated to emulate that of a mass attached to a string. Each node has a mass value associated with it (which would be the mass hanging off of it in the real world kit), and this influences how much force that node is applying to the main circular object. Moving the nodes or changing their mass value will influence the position of the ring, as it effectively represents the center of mass of the entire system through the forces applied by the nodes.

Using a simple example, I was quickly able to run a test to double check if the values provided by this toy were matching that of the true calculations and they did for the single test I’ve run so far. The center of mass equations of a system of particles was used, which can be found here on Wikipedia: Center of Mass – Wiki

DIGM 540 – Engineering Statics Game Project – Forces in Unity

digm520admin / / DIGM540, engineering, Forces, GameDesign, Physics Engine, Scripting, StaticsGame, Unity
May 23rd, 2018

Forces in Unity

AddForce – Unity Official Tutorials

Youtube – AddForce – Unity Official Tutorials

The bare basics of scripting forces in Unity with rigidBody’s. The AddForce can take two inputs: a vector for direction and magnitude, and what type of force it is.

ForceModes
  • Acceleration – Continuous change; not affected by mass
  • Force – (Default) Continuous change; affected by mass
  • Impulse – Instant change; affected by mass
  • VelocityChange – Instant change; not affected by mass

AddTorque – Unity Official Tutorials

Youtube – AddTorque – Unity Official Tutorials

The bare basics of scripting torques in Unity with rigidBody’s. The AddTorque can take two inputs: a vector axis to apply torque around, and what type of torque it is. This is very similar to AddForce. Important to remember, Unity uses “LEFT HAND RULE” for rotation.

ForceModes
  • Acceleration – Continuous change; not affected by mass
  • Force – (Default) Continuous change; affected by mass
  • Impulse – Instant change; affected by mass
  • VelocityChange – Instant change; not affected by mass

Game Ideation

Engineering Teaching & Research Equipment – Armfield

EF-1.1 – Statics – Forces experiment kitYoutube – EF series video Statics Forces 1 1 3

The links are similar videos, the first is just directly to the site of those that made the kit, and the second is a Youtube link. This kit provides hand on experience for understanding a lot of topics based around static equilibrium. Topics such as 2D shape center of masses, force vectors, and much more can be covered with this tool.

Game Idea – Engineering Statics Game

Following the general idea of the kit shown above, the game environment is populated with nodes that apply controllable forces to a central ring object. These forces can be altered to change the position of the ring. The environment will spawn collectibles that the player must direct the ring towards in order to collect them. To move the ring, they will need to intelligently alter the forces applied by the nodes.

Quick sketch of concept

Talks Every Game Designer Should Watch

digm520admin / / Game Feel, GameDesign, Talks
May 23rd, 2018

Talks Every Game Designer Should Watch

These are two talks suggested to be seen by anyone in game design by Squirrel Eiserloh in his talk “GDC 2015 – Math for Game Programmers: Fast and Funky 1D Nonlinear Transformations”. They cover general game design topics on making your game feel good no matter the concept.

Juice it or lose it – a talk by Martin Jonasson & Petri Purho

Youtube – Juice It or Lose It

Jan Willem Nijman – Vlambeer – “The art of screenshake”

Youtube – The Art of Screenshake

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 / / 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 / / 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 / / Game Design Community, Game Jam, GameDesign
May 2nd, 2018

Game Jams in Ludem Dare

Ludem Dare Site

“Ludum Dare is one of the world’s largest and longest running Game Jam events. Every 4 months, we challenge creators to make a game from scratch in a weekend.”

This site lets users participate in a game jam every 4 months where they create a game based on a theme over one weekend. The users can see others submissions, and it is a competition where submissions are based on multiple categories.

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.