Procedural Generation

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2021-04-01T22:27:38-07:00

Generated Games for the Game Boy

One project that I’ve been working on lately is generating games for the Game Boy.

The generator builds games using GB Studio.

Why the Game Boy? There were a number of reasons, including it being a fixed target platform with a really good emulator ecosystem. Since this is part of a larger project to research new ways of building generators, it made sense to target a platform that didn’t require a lot of scaffolding just to display something on screen. Plus, it lets us build games for genres that haven’t had as much game generation attention. A generator targeting Pico8 or Bitsy might be similar, though the other advantage of GB Studio is that it has a lot of useful data structures already implemented (like scenes and triggers) and so we can concentrate on teaching the generator how to use them.

I’m not the only one working on the project, so I certainly can’t take all of the credit. And it is definitely a work in progress - the next version of the generator is using a different approach that makes the process of building the generator much more flexible.

https://isaackarth.com/games/rom_gen_test_5/

questions to ask about your generator i recently

2021-03-05T13:01:37-08:00

Questions to ask about your generator

I recently ran across this list of questions I came up with during my Roguelike Celebration talk a couple years ago, and I thought that it stands up pretty well on its own.

Individual: What effect does a single artifact have? What are the differences between artifacts? What makes this artifact stand out?

Gestalt: What is the overall impression of the generative space? What is the effect of the group of artifacts?

Repetition: What are the similarities between artifacts? What does the player learn about the generator from their commonalities?

Structure: What does this artifact tell us about the generative space? What processes can we discern? What can we predict about the next artifact we see?

Surface: What direct impact does this artifact have on our experience? What details do we pay attention to?

Multiplicity: How much variance can we perceive across the generative space?

Cohesion: Do the artifacts feel like they belong? Do they fit in with the other artifacts? Can we perceive them as part of a cohesive system?

Style: How well do the artifacts fit their context? Can the generator adjust to match the current needs?

Are there other questions that you ask about your generators?

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2021-02-19T14:00:38-08:00

Explaining WaveFunctionCollapse

This video from Martin Donald is a great explanation of the WaveFunctionCollapse algorithm. I particularly appreciate that it also talks about calculating the adjacency constraints, which often gets overlooked when talking about WFC: There are many ways to implement both the solver and the adjacency calculations.

I also appreciate that Martin’s description digs into some of the data structure thinking that goes into actually implementing a version of WFC. I find that tends to trip many people up when they try to actually program the thing.

christmas carols blob opera in the

2020-12-25T07:00:34-08:00

Christmas Carols - Blob Opera

In the lower-right-hand corner of Blob Opera there’s a Christmas Tree slider. Take a look!

https://artsandculture.google.com/experiment/blob-opera/AAHWrq360NcGbw?cp=e30.

Alternate Rudolphs

2020-12-21T14:31:29-08:00

lewisandquark:

I’ve been experimenting with generating Christmas carols using machine learning algorithms of various sizes. The smallest AIs, trained from scratch on a set of carols, tended to get confused about what exactly the carols are celebrating. GPT-2, larger and with some internet pretraining… still got confused. Its carol about Rudolph “the All-Gracious King of All the Earth” was unsettling enough before Joe Zieja’s dramatic reading and The Forever Now’s eerily calm arrangement.
Now in 2020 we have GPT-3, trained on so much of the internet that my challenge is now to stop it from simply repeating carols that it has memorized. Give it just three example carols and it will respond with another from the internet, which is technically exactly what it was rewarded for during training. To get it to generate new carols, I discovered I could give it three existing carols (I chose Hark! The Herald Angels Sing (“bruise in us the serpent’s head”), All My Heart This Night Rejoices (“sin away and for aye full atonement maketh”), and Run Run Rudolph (“and away went Rudolph a whizzing like a merry-go-round”)), plus just the title of a nonexistent carol. It would generate, for example:
Mild is Rudolph
Mild is Rudolph’s image in the snow
He has a shiny nose and knows how to cozy-up-nose
He has a little red nose and tiny little hooves
He persuaded a mischievous infant to go straight to sleep
Goodness, how downy is Rudolph’s hide
He has, oh his forehead, so plump and smooth and divine
He has a little black snout and tiny little hooves
Benevolence makes foolish young children go straight to sleep
Shy is Rudolph’s figure when he’s near
He has a twinkle, oh in his eye, and he’s kind of lean
He has a bright shiny nose, and tiny little hooves
He can make any child go straight to sleep
The AI has seen the existing Rudolph lore many times, however, so it would tend to drift back into singing Rudolph the Red-Nosed Reindeer. Sometimes it would only drift back partway.
The Carol of the Rudolphs
Rudolf the red-nosed reindeer
had a very shiny nose.
He kept it polished nice and clean with a white piece of soap.
All of the other reindeer
used to laugh and joke,
and call him names like ugly, Zonker, Dunderhead.
But their laughter stopped
when they saw him get
him speed on down the runway.
For he could change the course
of the wind
with his nose, and people called him
Very Bright.
Rudolf, the darting reindeer
knew the trail through the woods,
even in the fog he could spot his mark.
Eeee-merry, eeee-merry Christmas,
Yes, that is his song.
He could steer a craftsman’s plane,
Could lead a camel home out of a trance.
His tender little bat-like ears
could hear a whisper, from ten miles away.
He could roar like a lion.
He could fly, gliding with ease.
His little rusty wheel, he would use
to help create the most wonderful wheel of cheese.
Yes, Rudolf, the cherry nosed reindeer,
could do all of these things and more,
But the only thing his nose could
not do was smell. Delectable Waffles
Who knows what’s in Rudolph’s nose?
Only Rudolph knows –
And Rudolph won’t tell!
The “sing the praises of Rudolph” directive seems to come through very well, especially if I give it an appropriate title.
All Hail to Rudolph
All hail to Rudolph
The legend of Christmas
The most famous reindeer of all
All hail to Rudolph
Happy all the year
From everyone we see
Heroes come and heroes go
But Rudolph stays forevermore
Oh boys and girls of every age
Can’t you hear the sleigh bells ringing?
Oh look! There’s Santa and Parson Brown
Defying the laws of time and space
Now prepare yourself and your family my boy
Prepare to get your Christmas fix
Now Rudolph goes smashing through the roof
Just like a power lift with a candy-cane moustache
And the deer’s nose is dead-on, deadly accurate
Pow! Now it’s Rudolph versus Mars
All hail this joyful night
All hail the night folks sing
All hail to Rudolph
The legend of Christmas
The most famous reindeer of all
O Come Rudolph, Come
O Come Rudolph, Come Ye Faithful
Oh come, come holly, clad in white, Oh come ye faithful.
Oh come aloud! Oh come and join our choir,
Oh make a joyful noise till earth and heaven ring.
Caw caw caw oh, caw caw caw oh, Caw holly, Caw plum this Christmas.
Oh, caw caw caw, oh caw oh, caw oh oh oh, oh.
Even if I give it a title like “Rudolph is the Worst” it knows that the story of Rudolph ends with redemption. (That is, unless it goes into truly awful territory, there being a lot of ways of being the Worst in its internet training data.)
Rudolph is The Worst Reindeer
Rudolph the red-nosed reindeer
Was a stubborn old reindeer.
Never would you find him
Participating in Christmas.
Never entered his head,
Of decorating a sleigh.
Then along came Bumbleboo
One foggy Christmas Eve,
And he said, “Rudolph with your nose so bright,
Won’t you guide my sleigh tonight?”
Then all the other reindeer,
Gathered round to admiere him,
And they nodded their heads,
As they shouted out in cheer,
“Rudolph the red-nosed reindeer
You’ll go down in history.”
Other times it seemed to be going for counting songs and, like GPT-2, is weird when it counts things. Here my title prompt was “Rudolph of”.
Rudolph of Orange (Medley)
There are 15 different colors,
That you see within the shirt.
There is a booklet,
Which identifies each one.
You’re looking at a number one,
While the kids are knocking on your door.
Then we started laughing,
When we realized that Rudolph was near!
Courtesy of the index theory,
There are only twenty eight!
July is the eighth,
And that’s when Christmas is!
REFRAIN:
And 1 wrapped a nose
(Time now for teasing and for fun)
2 pictures, horns a cap,
3 hooves and legs and feet,
4 antlers sweeming!
5 eyes,
6 nostrils,
7 buttons, zipper,
8 bolts and bumpers,
9 flashing lights on his cap,
10 red nose,
11 furry chin,
12 rubsy ears,
13 plastic eyes,
14 laughing so joyously.
15 steps and a twisted reindeer train!
16 frosted incislotes,
17 dreamily red bow,
18 nose light,
19 velco on reins,
20 candy apple red.

Two reindeer he’ll need to pull him through!
It’ll be commercial Christmas,
For years to come,
When Rudolph gets his new reindeer team.
For a few more carols (including “O Little Rudolph” which involves ghosts and ghouls somehow), enter your email!

More of Janelle Shane’s neural network shenanigans, this time with GPT-3 generated holiday cheer!

blob opera david li and google arts and culture

2020-12-15T19:39:42-08:00

Blob Opera

David Li and Google Arts and Culture made this little machine-learning experiment to create opera-singing blobs.

The music is created with a machine learning model that learned about opera by listening to Tenor, Christian Joel, bass Frederick Tong, mezzo‑soprano Joanna Gamble and soprano Olivia Doutney (plus Ingunn Gyda Hrafnkelsdottir and John Holland-Avery). It’s not playing their audio–rather, it’s playing what the model thinks opera music sounds like based on analyzing 16 hours of their singing.

I like how the eyes watch the cursor - that’s one of those little procedural animation touches that’s simple but adds a lot of life.

https://artsandculture.google.com/experiment/blob-opera/AAHWrq360NcGbw?cp=e30.

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2020-12-09T19:07:56-08:00

How Watch Dogs: Legion’s ‘Play as Anyone’ Simulation Works | AI and Games

Tommy Thompson’s deep dives into AI in games frequently overlaps with procedural generation in games, and that’s certainly in the forefront of this latest video about the characters in Watch Dogs: Legion.

One thing that struck me is how the systems in the game are building on previous research. For example, the player-time character generation is partially inspired by research on alibi generation by Ben Sunshine-Hill and Norman Badler.

I’d like to know more details on how the generation of character details, because their ability to generate from partial data sounds like they’re using some form of constrain solving but I’m not sure.

It’s also worth pointing out that they don’t try to generate everything from scratch: recruitment missions, for example, have some basic narrative scaffolding that cab have the dynamic elements slotted in. This is a smart way to handle it: you don’t need to generate everything, and writing some solid content saves you time for enriching the generative system in ways that pay off more effectively.

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2020-12-07T09:12:49-08:00

Procedural Map Generation Techniques (Roguelike Celebration 2020)

This is a great survey of roguelike map generation techniques by Herbert Wolverson, covering a ton of different techniques. He uses animated illustrations to demonstrate how different algorithms work: random room placement, BSP subdivision (used by Nethack), cellular automata (good for caves), drunkard’s walk (aka random walk), diffusion limited aggregation (including adding attractors and symmetry for more control), Voronoi cells (plus using different distance algorithms), Perlin noise (commonly used for landscapes, but Ken Perlin’s invention is useful for a lot of stuff), and the use of prefabs (or “vaults”). He proposes the use of a Dijkstra map as a way of reading the map so you can do things like remove unreachable areas, or place the starting point in a reasonable spot, or figure out which parts of the map are on the golden path.

One important thing Herbert highlights is the importance of context and control in designing a level generator: the randomness is “fed into an algorithm that that approximates what you want to get but ensures that it is different every time.”

He also talks about combining generators, which is something that can get lost when cataloging different techniques: there’s no one perfect algorithm for generating a roguelike map, and combining multiple techniques can result in something much more interesting than a single generative operation could make on its own.

His tutorial for making a roguelike in Rust has a lot of universally applicable algorithmic stuff, including even more map generation algorithms, so there’s also lots of material if you want to go beyond the already information-dense talk itself.

Overall, this is a great starting point for expanding your toolkit of map generation techniques. I particularly like Herbert’s examples for controlling the maps and combining techniques: there’s a lot of scope for getting creative with how you put together different elements of your map generator–particularly if they also tie in with other parts of your design or narrative.

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2020-12-04T09:01:02-08:00

There’s a Skull in My Garden

How can you get players to care about the things your generative system makes?

In this short Roguelike Celebration talk, Younès Rabii talks about player interpretation and meaningful stories, by way of his roguelike Tea Garden.

Tea Garden is influenced by Max Kreminski’s idea of gardening games, which posits a style of games that treat generative things as non-disposable, in contrast to the common “we’ll just generate another one” style exemplified by the extractive gameplay in things like Minecraft.

In Tea Garden you explore gardens in your dreams, and bring back dream flowers. Which means it has a flower generator. Problem: many of the flowers just looked like random jumbles of pixels. However, by adding interaction configurations of pixels that didn’t seem to have meaning created new avenues for those random flowers to be put in a new context and be perceived as meaningful.

Tea Garden has a very neat flower generator (though Younès does point out that it has some risks, hence the eponymous and entirely unexpected skull). But the larger point of creating context through interactivity is related to a lot of ideas I’ve been thinking about.

Generating something with context means that it exists as part of a larger system. Objects in the real-world have their history embedded in every scratch and smudge, so Dwarf Fortress’s history-tracking emulates some of that effect. A generator’s framing brings in pre-established context, borrowing from the rest of the world. Sharing content imports the context associations that other people have established into your own experience. And, in Tea Garden, the interactions create the affordances that let you impart your own context to the generated flowers.

faces of humanity a project of face drawing

2020-12-03T17:08:45-08:00

Faces of Humanity

A project of face drawing generation using “Quick, Draw!” game data set from Google Creative Lab, millions of drawings from all around the world!

Each part of a face was drawn by a different person, at a different place, at a different time.

An unexpected global collaborative project with millions of contributors :)

One new face generated every hour on the project website : http://project.laboiteatortue.com/facesofhumanity

The project tumblr : https://facegenerator.tumblr.com

Your question are welcome.

Cheers

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This project got sent to me a while ago, but I think it’s a neat application of public data in a generative way. This use is reminiscent of Emily Short’s Beeswax–in this case, the use of crowdsourcing a corpora of drawings.