# Towards Procedurally Generated Stories via Free Monads

Strongly inspired by Dave Laing’s fantastic series Cofun with cofree comonads. This post and the next are mostly rehashes of (superior) blog posts of his, but there is novel material to be covered soon.

I am eternally torn between a dichotomy I like to call “finish shit vs. solve cool problems.” Finishing shit requires a lot of polish around the boring corners of a project, after all the cool stuff has been solved – and there’s always another cool problem waiting to be solved.

In particular, this dichotomy has recently manifested itself as “I should make an RPG vs. I already know how to make RPGs, it’d be fun creatively but gee that sounds like a lot of tedium and wouldn’t teach me much in the end, except for maybe the value of hard work.”

Then I realized making an RPG doesn’t need to be tedious and boring. I’ll just teach a computer how to generate an RPG. Goldmine genius idea. I don’t know how to make a procedurally generated RPG, but really, how hard could it be?

This post is the first of many on just how hard it can be.

## The Motivation

People have been making roguelikes for decades. While roguelikes are spectacularly cool, they’re not really RPGs in the more common sense of the word. There’s no narrative to a roguelike, you just adventure around and kill shit.

The RPG that inspired me to make an RPG was Earthbound, which is known for its quirky atmosphere and for managing to pull of some sort of weird-humorous-plot-mixed-with-lovecraftian-horror juxtaposition. Earthbound *feels* like it might have been made on drugs, but somehow manages to still be a fantastic experience.

*This* is the kind of thing I want to generate. Lots of games have tried to generate interesting worlds and plots, but, at least when I was in the games industry, the state of the art was prefabricating modules and stitching them together. Sure, it’s hard to generate solid plots, but I don’t think its intractable.

I think the problem might have been this: this problem is fundamentally functional; any imperative approach is going to be a Bad time.

Maybe. Lots of this is vaporware still, but it *feels* right, and I have a plausible path to actually executing on it.

## The Idea

Enough run-around. Are you ready to hear my revolutionary idea to generate procedural RPGs with coherent and interesting stories?

- Build a datastructure representing a story.
- Turn this datastructure into a game.

Amazing, right?

Okay, not that amazing, but here’s the rub. Instead of building this datastructure by hand, we’ll write a domain specific language (DSL) which will generate the datastructure for us. And then if we then embed this language into Haskell, we’ll lift all of the expressiveness of Haskell into our DSL. If we limit the DSL to a few number of primitive operations, and implement all of the interesting pieces as combinators on top, it will be easy to abstract over, and more importantly, to interpret.

This interpretation step is, unsurprisingly, where the “magic” happens.

Separating the *structure* of what we want to do (which is what the DSL provides us) from *how* we do it means we can do different things with the same data. For example, given a basic plot skeleton, we can run over it, and with the help of a random number generator, determine a theme. Then, given the theme and the plot skeleton, build necessary locations to help advance the plot from one scene to the next. Then, with all of this, we can build intermediate landscapes to stitch the locations together. And so on and so forth.

There are lots of potential failure modes here, but the approach seems feasible.

## The Pieces

So I went through a bunch of games whose stories I adore, and I attempted to deconstruct them into their primitive operations. A simplified datastructure of what I came up with is provided here:

```
type Story = [StoryPrim]
data StoryPrim = Change Character ChangeType
| Interrupt Story Story
data ChangeType = Introduce
| Die
| Leave
| Arrive Location
| Kill Character
| Learn ChangeResult
data ChangeResult = ChangeResult Character ChangeType
```

which is to say, a `Story`

is a list of `StoryPrim`

s, which is either a change in character state, or one `Story`

being interrupted by another (eg. if someone’s murder attempt is foiled by an unexpected arrival.)

This isn’t comprehensive enough to generate entire stories, but it’s definitely good enough to motivate the remainder of this post.

## Free Monads

Let’s take a little break and talk some math.

Free monads are one of the neatest things I’ve learned about recently. The definition (in Haskell) of a free monad over a functor `f`

is this:

```
data Free f a = Pure a
| Bind (f (Free f a))
```

Which can be thought of a recursive datastructure which bottoms out with a `Pure`

or recurses with an `Bind`

. The definition was hard for me to work my head around, so let’s give it a concrete functor and see what pops out:

```
data Free [] a = Pure a
| Bind [Free [] a]
```

If we squint, this is actually just a tree where `Bind`

is a \(n\)-ary branch, and `Pure`

is a value at a leaf. So a tree is just a special case of the free monad. That’s kinda hot, if you’re as into this stuff as much as I am.

But what’s more hot is that given any `instance Functor f`

, `Free f`

forms a monad:

```
instance Functor f => Monad (Free f) where
return a = Pure a
Pure a >>= f = f a
Bind m >>= f = Bind (fmap (>>= f) m)
```

It’s probably the dumbest `Monad`

instance imaginable, but hey, it adheres to the monad laws, and that’s really all we ask for. The laws are satisfied only in a very trivial sense, in that all we’ve done here is encode the rules of `return`

and `(>>=)`

into our datastructure which is to say, we haven’t done any processing yet. We’ll return to this in a moment.

It’s called “free” for exactly this reason of trivially satisfying the laws – given any functor `f`

we can get an (admittedly stupid) monad over `f`

for free.

Because our free monad is just a datastructure in the exact shape of a computation we *would* want to carry out over its contents, it’s easy to write an interpreter for it. Or several.

See where I’m going with this?

Here’s the kicker: **Free monads turn out to be the abstraction behind DSLs because they encode the structure of a computation, without imposing an interpretation over it.**

But remember, getting a free monad requires having a functor. If we can find a means of encoding our `Story`

grammar above as a functor, we can lift it into a DSL via `Free`

.

## The Command Functor

So we need a means of getting a `Functor`

instance for our `Story`

type described above. But how?

Let’s start playing madlibs with what we know.

```
type Story a = Free StoryF a
data StoryF a = -- ???
```

Looking at the definition of `Free`

specialized over our functor `StoryF`

once again hints us in the right direction:

```
data Story a = Pure a
| Bind (StoryF (Story a))
```

The polymorphic variable of our `StoryF`

functor is only ever going to be a `Story a`

, which is to say a pure `a`

or a bind computing more of the final value.

So our polymorphic type variable is the type of the continuing computation. Because `Pure`

from `Free`

takes care of how computations terminate, our functor should always have a continuing computation. Voila:

```
data StoryF a = Change Character ChangeType (ChangeResult -> a)
| Interrupt (Story ()) (Story ()) a
```

contrasting this against our old `StoryPrim`

, we’ve just added a new product involving `a`

to all each of our sum terms. Again, `a`

should be considered to be the type of the continuing computation.

But what’s this funny `ChangeResult -> a`

thing? Well, recall that we wanted a `Change`

to return a `ChangeResult`

indicating what changed, which is to say this result should be a *parameter* to the rest of the computation – thus our function type^{1}.

`StoryF`

is what’s known as our command functor, because as we will see, its constructors will eventually act as commands in our DSL.

But wait! Not so fast. We haven’t yet provided a `Functor`

instance for `StoryF`

. It’s trivial, but we present it here for completeness:

```
instance Functor StoryF where
fmap f (Change c ct k) = Change c ct (f . k)
fmap f (Interrupt s s' k) = Interrupt s s' (f k)
```

And so `StoryF`

is now a `Functor`

, which means that `Free StoryF`

is a `Monad`

, which means that we can use `do`

notation inside of it! We’re most of the way to our DSL!

All that’s left is to lift our `StoryF`

data constructors into `Story`

constructors. The details of this are a little messy, but luckily `liftF`

from the `free`

package does most of the manual labor for us.

```
change :: Character -> ChangeType -> Story ChangeResult
change c ct = liftF $ Change c ct id
interrupt :: Story () -> Story () -> Story ()
interrupt s s' = liftF $ Interrupt s s' ()
```

and that’s it! Short of some helpful combinators, we’re done! We can now write basic stories in Haskell using `do`

notation!

```
myStory :: Story Int
myStory = do
let mandalf = Character "Mandalf the Wizard"
orcLord = Character "Orclord Lord of the Orcs"
orcBaby = Character "Orclord's Child"
sadness <- kill mandalf orcLord
change orcBaby $ Learn sadness
return 5
die :: Character -> Story ChangeResult
die who = change who Die
kill :: Character -> Character -> Story ChangeResult
kill who whom = change who (Kill whom) <* die whom
```

As far as stories go, one about a child learning of its father’s death is probably not going to win any feel-good-novella-of-the-year, but the example serves to showcase several things:

- We can build abstractions with standard Haskell combinators (eg. killing someone implies that they die.)
- The fact that this typechecks shows that our language is expressive enough for characters to learn of the arbitrary actions of one another (including learning that they’ve learned something.) Furthermore, knowledge is first-class and can be passed around the story however we see fit.
- Like all monads, our DSL can describe things that happen
*while*returning potentially unrelated data. The \(5\) above is meaningless, but allows us to interleave story descriptions with arbitrary computations.

This seems like a good place to stop for today – we’ve covered a lot of ground. Next time we’ll discuss how we can use cofree comonads (I am *not* kidding here) to build an interpreter for our DSL.

If this isn’t immediately evident to you, make sure you understand how

`do`

desugaring works.↩