In Algorithmic AR Part 1 I outlined the concept of Algorithmic AR and gave a brief tutorial on how Factories work in ARIS to make games based on place-based algorithms. The tutorial made use of an existing game Rupee Collector to illustrate those features.
In this follow up, I show some ideas for how to take Factories further through a couple other examples, one of which is an early game design assignment I give my students each year. There’s also a brief discussion about Algorithmic AR in the broader world, where we might begin to think of it as a general interactional paradigm through existing designs and the ones we learn to create.
Your Turn – Remixing Rupee Collector
Once you’ve played Rupee Collector and seen how factories actually work in ARIS to make locational content algorithmically, it may time to think about making a new game using Rupee Collector as your inspiration to really dig in. Especially if you teach game design—even without ARIS—I have an assignment you can do, and take and remix for your own use; it is based on this game and thinking about remixing its design.
To actually begin remixing Rupee Collector as the first part of the linked activity, or to simply further explore how to use factories, we can start with an example of a remix I made with Breanne Litts and Phil Dougherty, It’s Dangerous to go Alone.
Did you play it yet?
Great. I knew you would. Let’s talk about it.
It’s Dangerous to Go Alone
It’s Dangerous to go Alone is different than Rupee Collector, but not a lot. It is based on the question, “Could you make a game that actually required teamwork instead of just competition?” In general, there are lots of ways to make team-based games, but in ARIS, the options are limited. In particular,
- Locks, the logical glue in ARIS, largely respond to what an individual player has done or has in possession. The world is largely not shared between players.
- Players cannot easily interact with each other in ARIS. We used to have a cool “trade” feature, but Google bought it and shut it down.
What you can do in ARIS is funnel a player into one of many multiple roles. In It’s Dangerous to go Alone, you choose to be a miner or hunter based on the answer to a single question and the resulting item in your inventory. Then the rest of the locks in the game respond to whether a player has one item or the other. The miner is the unnamed role from the original Rupee Collector, the one who picks up rupees. But the wrinkle is that this world now contains two algorithmically generated bits: rupees and moblins. The moblins will kill the miners, but the hunter can kill the moblins. A team of two will be able to proceed safely.
Factory Use #2: Different but together. It’s Dangerous to go Alone is an example of how you might create chances for players to see worlds different but complimentary ways and go together.
The only real awkward bit is that the hunters cannot see the rupees. While you can make objects respond differently to different players, you cannot make them visible but unactionable. I originally wrote about this game and its design here if you’re interested.
One of the factory options I glossed over so far is the basis by which you determine the maximum number of triggers a factory places into the game. In Rupee Collector, this maximum is determined per player. Each person gets their own ring full of rupees. But you can also set a maximum number of triggers for all players of the game put together. That is, you could create a game where only the first or most recent player would get a rupee. Instead of setting the maximum “per player” in the factory options, you simply set it to “total”.
I made a small game to feel out the possibility presented by this option to set a global maximum. It’s called Golden Goose. In it there is a single item (Golden Egg) which is handed out by a single plaque (Golden Goose). The factory that generates triggers for this plaque is set to produce a maximum of 1 total—The Golden Goose only lays a single egg—every 10,000 seconds (a bit more than three hours). The egg is laid directly on top of the player who generates it so that whomever logs into the game first, at least 10,000 seconds since the last time someone logged in, immediately sees the Golden Goose and gets a Golden Egg. Take a look at the relevant factory settings.
Notice that the egg is laid very near the player (1–25 meters) with a large availability (1000 meters). This means the player doesn’t have to go get the Goose.
I made this game so that anyone around the world could play, and do so quickly and easily (all you need to do is log in to the game), and first wrote about it here. There was also a leaderboard so that a player could see who had the most eggs. Unfortunately, the leaderboard has not made it to ARIS 2.0 yet (or to any of these games).
Factory Use #3: Competition for limited resources. Factories allow you to regularly parcel out resources for your players. You can use this to create interesting competitive situations.
Los Duendes is a rather different kind of example of factories. Rather than using factories to create new kinds of locative gameplay, this game uses them to make a location specific game playable anywhere in a more real way than through “quick travel” (i.e. traveling by touching icons on the ARIS map screen). Los Duendes is a ghost story set in the small town of Truchas, NM and based on folklore from there. The students working on the game wanted to be able to show it and the gameplay they designed, and not just for players in Truchas. So instead of setting the locations that make the story unfold by hand, they created factories to make the game playable by walking, but starting at any location.
Trying to make a game playable anywhere (or at least in many places) algorithmically opens new design questions. A location might spawn inside a building, for example. How do you work around that possibility?
Factory Use #4: Make a story that unfolds through physical play so that it can play out anywhere. People have played Los Duendes, and shared the folk stories of a small town in northern NM in the way designed, across many states. Off site play was previously something only simulable by touching icons on a small screen.
What Is Algorithmic AR?
Beyond the how-to and pedagogical examples of factories, this feature of ARIS opens up new questions for what AR can be and can be about. We are not the first ones to do something like this. Probably the best known game in this area was Parallel Kingdoms. It has been around almost as long as the iOS app store itself—essentially the zero year for modern mobile game design. It’s a cool game and you should give it a try if you haven’t played it before. Warning: It’s rather deep. But this one game doesn’t exactly plumb all the possibilities for playing around with the idea of somewhat random physical locations. Neither do the examples above exhaust the space.
There is still a lot to explore here, and it gets to the heart of some of the basic questions about AR generally. How are we able to differently able to explore, get to know, and act in the places around us? How can we learn to play in new ways in our surroundings? What will be fun? What will bring people back over and over? What will get people outdoors over and over? Where do creations like these best fit within and serve to improve people’s lives? More specifically, what does the gaminess of an algorithm add to the ability of us to augment reality using a mobile device?
Do factories allow us to realize the dream of “write once, play anywhere”, or does it bring us back to a place of blandness, where we aren’t really leveraging place in our designs and instead are only staring into tiny screens? How many shattered screens and pulled hamstrings lie before us on the way to answering these questions? I hope you’re encouraged to help us find out.