See also: Cosmos Boulders ∘ Rux Rolli ∘ Destructorizers
An experiment in reactive framework design that tries to answer the question: What happens if you take UML state machines and make them purely functional by following The Elm Architecture?
We call the resulting construction a nested modal transducer assemblage.
The term "transducer" is taken from automata theory and is basically unrelated to what's called a transducer in Clojure or in Scheme; "nested" stresses embedding one transducer in another, as opposed to feeding the output of one transducer into the input of another; and "modal" stresses that each transducer is, like a state machine, in exactly one of a finite number of control states at any given time.
Code samples in this document are given in a purely functional pseudocode that may look vaguely like Haskell, but the resemblance is only skin-deep. Runnable code for these examples can be found in the accompanying source files:
- Haskell: NestedModalTransducers.hs
- Scheme: nested-modal-transducers.scm
Note that this repository contains only a description of the theoretical framework. Applications of the framework, should they ever come to pass, will be listed in the Related resources section below.
I was thinking about ways to model a certain class of reactive systems (Footnote 1) and I concluded that the most appropriate model was a state machine extended with the following four properties:
- The machine must keep track of more state than just the finite "control state" that the machine is in. This additional state is unbounded rather than finite, and it can affect how the machine transitions.
- It must be possible to arrange machines in a hierarchy, where a single state of an outer machine can contain multiple states of an inner machine.
- It must be possible to define arrays of machines which transition independently: a single outer state might contain, not just a single inner machine, but a set of inner machines.
- While an inner machine is primarily responsible for its own operation, the machine it is contained in should be able to oversee and manage its operation as necessary.
I'm not a huge fan of UML, but UML state machines (descending, as they do, from Statecharts) turn out to have exactly the first three properties, which they call "extended state", "hierarchically nested states" and "orthogonal regions" respectively.
UML provides some tools to address the fourth property, notably "entry and exit actions", which have been compared to constructors and destructors in object-oriented programming. You could think of this as RAII for states: sensible entry and exit actions allow sensible transitions between inner states, even when they are deeply nested in different parts of an outer state machine.
But the facilities provided by UML for this purpose do have limitations. The root condition here is that UML state machines inhabit an imperative, effectful paradigm: just about anything can trigger an arbitrary "action" which does not have limits to its scope. So the transitioning of an inner machine may cause side-effects, and there is nothing the containing machine can do about this.
In addition, for my purposes, I wanted a theoretical framework that would make reasoning about these reactive systems as tractable as possible. (Footnote 2)
So, suppose UML's actions were not unrestricted effects, but instead were symbolic values, intended to be passed around during evaluation, only to be translated into effects at some later point.
This would allow both things: it would let these hierarchical assemblages of state machines be expressed in a purely functional manner, and it would permit management of inner machines by outer machines, in the following way: the inner machine can no longer execute arbitrary actions itself — it must return them to the outer machine, which gets a chance to veto or modify them before they are actually executed.
So, the challenge here is to make something that, if not equivalent to UML state machines in their entirety, addresses the above four properties at least as satisfactorily as UML state machines do, and is purely functional.
In Redux, your application's logic resides in pure functions which have the type
f : S × A → S
where S is the countable set of states that your application can take on, and A is the countable set of actions it can respond to. The actions are just inert symbolic descriptions of actions; they don't contain any logic themselves.
Meanwhile, in The Elm Architecture (or redux-loop) you can write pure functions of the type
f : S × A → S × C
where C is the countable set of commands that your application can issue. In the same vein as the actions, these commands are just inert symbolic descriptions of effects. Some interpreting step that comes after evaluating the function must enact those effects.
The first type of function is called a reducer because, according to the Redux docs, "it's the type of function you would pass to Array.prototype.reduce(reducer, ?initialValue)." So there is a whole theory and practice of folds that can be drawn on when working with reducers. Indeed, you can think of a reducer as a fold over a lazy list of events — the events that are currently happening.
The reducer concept also corresponds extremely well to the transition function of various formal automata. The semiautomaton is the most general example, but it is also easy to see how one could write a finite automaton or a push-down automaton as a reducer (even though these automata are not often employed in a "reactive" setting).
These are all automata that, like reducers, do not produce outputs or have effects of any kind. An automaton that does produce outputs is called a transducer. Specific kinds of transducers include Moore machines and Mealy machines.
The second type of function above (the one from Elm) maps well to the transition function of a generalized transducer. The main difference is that, in automata theory, the outputs of a transducer are often thought of as directly effectful: signals sent to other devices, for example. But here, they are merely symbolic descriptions of effects — values that something else will observe and execute later on.
For brevity, we will call the transition functions of transducers, also transducers, where there is no danger of ambiguity. To avoid the clash between "actions" in Redux and "actions" in UML state machines, we will stick to the terminology used for transducers: things that go into a transducer are called its inputs, and things that come out of a transducer are called its outputs. Our outputs will be values which describe effects, but we'll avoid calling them "effects" because they are not the effects themselves. ("Command" might be okay, because it's used in Elm and it evokes the command pattern, which is relevant; but for consistency we'll avoid it too.)
Also, it will be useful (for reasons explained below) to think of any given transducer as producing a list of outputs. Such a list may be empty. The signature of our transducers can now be written as:
f : S × I → S × [O]
As a contrived example, suppose we have a light switch that rings a bell as a side-effect when you turn it on.
lightTransducer mode input =
case (mode, input) of
(On, TurnOff) ->
(Off, [])
(Off, TurnOn) ->
(On, [RingBell])
_ ->
(mode, [])
As a way to test this, you might want to run it on a list of inputs, to obtain a final state and a list of outputs.
rehearse transducer state [] = (state, [])
rehearse transducer state (input:inputs) =
let
(state', outputs) = transducer state input
(state'', outputs') = rehearse transducer state' inputs
in
(state'', outputs ++ outputs')
You would then expect that, for example,
rehearse (lightTransducer) Off [TurnOn, TurnOff, TurnOn, TurnOff] =
(Off, [RingBell, RingBell])
In a practical setting though, you probably want this to be "reactive", in that you want to consume inputs when they are received, and enact effects when they are produced. You would have some top-level driver code for that, and it might look like:
reactWith transducer state =
let
input = waitForInput
(state', outputs) = transducer state input
_ = enactEffects outputs
in
reactWith transducer state'
Redux provides ways to combine reducers, and advocates that you combine reducers hierarchically until your entire application is, at its core, one big reducer.
Redux defines a standard way to combine multiple reducers into a single reducer. This standard way is not the only way to compose reducers, and in many contexts it is probably not the "best" way either, but it establishes a pattern and a practice.
The analogous thing here is combining transducers. But there is an important difference from reducers: insofar as the order of effects matters, the order of outputs matters too, so the order in which transducers are combined matters. (Footnote 3) This is the reason why we defined our transducers as producing lists of outputs: lists already capture this ordering property. The empty list is also useful for indicating that a transducer did not produce any output.
The transducer equivalent of Redux's combineReducers can be written:
combineTransducers tA tB = tC where
tC (stateA, stateB) input =
let
(stateA', outputsA) = tA stateA input
(stateB', outputsB) = tB stateB input
outputsC = outputsA ++ outputsB
in
((stateA', stateB'), outputsC)
In Redux, the resulting combined state is a Javascript object, so the order of the keys doesn't matter. Here, since order does matter, our combined state is an ordered pair of the constituent states.
Combining transducers, in this manner and in other manners, lets us form purely functional assemblages of transducers.
So now we try to capture the four properties in the first section with the tools of the second section.
This part is easy; in the functional theory that we've brought over from Redux, S is an arbitrary countable set, so we can stick whatever we want in there. (Footnote 4)
Actually, the part we should be concerned about is the other way around: we want these transducers to behave, at their core, like traditional state machines. We don't want to abandon the fundamental idea of the machine being in one of a finite number of states at any given time; it's useful.
So we say that S is divided into a finite number of partitions, which correspond to the "control states" of the machine — the values the so-called "state variable" is allowed to take on.
In practice, S would typically be some kind of record type, and the "state variable" would be a field of that record, of "enum" type (or some moral equivalent thereof).
This highlights a terminological problem that we should address before it becomes serious. As soon as you add "extended state" to a state machine, the word "state" becomes ambiguous. When you say "state", do you mean the finite (and qualitative) "state variable", the unbounded (and quantitative) "extended state", or the state of the entire machine, taken as a whole?
For the current work, we will call this finite set of partitions of S, the modes of the transducer. At any given time, the transducer is in exactly one of these modes, and we will call that the current mode (or just the mode if the context is clear). What UML calls the "extended state", we will call the data of the transducer; and the state of the whole thing (mode and data together) we will call the configuration of the transducer. (Footnote 5)
In this way we can, in fact, completely avoid using the word "state" if we like. We might not go that far, because sometimes it is evokative, but we will go in this direction. One concrete step in this direction is that, instead of "state machines", we will talk about "modal transducers". Another concrete step is that the (nominal) datatypes that our example transducers will work with will no longer be "state"s, but rather "config"s.
Here we modify the previous example of the light, to make it record the number of times it was turned on:
countingLightTransducer config input =
case (config.mode, input) of
(On, TurnOff) ->
({mode:Off, data:config.data}, [])
(Off, TurnOn) ->
({mode:On, data:config.data+1}, [RingBell])
_ ->
(config, [])
We can implement UML's hierarchically nested states by embedding the transition function of the inner transducer within the transition function of the outer transducer, and at the same time embedding the configuration of the inner transducer within the configuration of the outer transducer (specifically, in its data).
In more explicit terms, call the inner transducer tI and the outer transducer tO. tI has a configuration cI made up of mode mI and data dI; tO has configuration cO made up of mode mO and data dO. Now we can say: A description of cI is embedded in some manner in dO, and we can think of this embedding as a pair of functions:
extractInner : cO → cI
embedInner : cO × cI → cO′
These have the usual form of "get" and "put" operations on a data structure in a purely functional language. Embedding cI in cO results in a new cO′, but extracting cI from cO leaves cO unchanged.
In practical terms, you can think of both cI and cO as some kind of record types (comprising mI and dI, and mO and dO, respectively), and one field of the dO record will be able to hold a cI record.
One important thing to remember is that tI is entirely contained within tO, so it is the responsibility of tO to transition the embedded tI as well. That is, somewhere in the definition of the function tO is an application of the function tI.
Typically tO will only transition tI when mO is a particular mode, however there is no strict requirement for that restriction. (Departing from it corresponds to "overlapping states" in the Statecharts formalism.)
To spell out how tO would typically manage the transition of tI:
- tO provides an input for tI (perhaps the same input tO received)
- tO decodes tI's configuration (cI) from its own data (dO)
- tO enacts the transition of tI (i.e. inside the transition function tO, the transition function tI is applied to the input for tI and the extracted configuration cI)
- tO encodes tI's new configuration cI′ back into its own data (resulting in a new data dO′ for itself, which is part of its new configuration, cO′)
- tO transitions itself based on its own configuration "as usual" (but we note that the new cI′ is also available to it for making this transition)
In pseudocode,
outerTransducer outerConfig input =
case (outerConfig.mode, input) of
...
(ContainingMode, _) ->
let
innerInput = obtainInnerInputFrom input outerConfig
innerConfig = extractInnerConfigFrom outerConfig
(innerConfig', innerOutputs) = innerTransducer innerConfig innerInput
outerConfig' = embedInnerConfigIn outerConfig innerConfig'
outerOutputs = obtainOuterOutputsFrom innerOutputs outerConfig'
in
(outerConfig', outerOutputs)
...
The simplest way to write obtainInnerInputFrom and
obtainOuterOutputsFrom would just be to take the same input
that the outer transducer receives, and have the outputs of
the outer transducer be exactly the outputs of the inner tranducer.
Neither of these need to be the case, though, and by choosing
more than trivial logic for these functions we will enable the
outer transducer to "manage" the inner one.
We'll cover this in more detail below.
As a more concrete example, here is a transducer that represents
a countingLightTransducer behind a door. The TurnOn and TurnOff
inputs will be sent to the light only when the door is open.
doorTransducer config input =
case (config.mode, input) of
(Closed, Open) ->
({mode:Opened, data:config.data}, [])
(Opened, Close) ->
({mode:Closed, data:config.data}, [])
(Opened, lightInput) ->
let
lightConfig = config.data
(lightConfig', outputs) = countingLightTransducer lightConfig lightInput
in
({mode:config.mode, data:lightConfig'}, outputs)
_ ->
(config, [])
Orthogonal regions are straightforward. If we have a fixed number of independent (and possibly heterogenous) transducers embedded within an outer transducer, we simply transition them all in the manner described in the previous section, combining the results as we see fit. (This is straightforward enough that I fear that a code sample might be more obscuring than enlightening; thus it is left as an exercise for the reader.)
We can extend this idea to an embedded array of homogenous transducers by
having a list of transducers and basically doing a map over it. Except,
we can't simply map because we need to accumulate a list of
combined outputs of the inner transducers; and if the order of outputs
matters, then the order in which we transition this embedded array will
also matter. The simplest approach, then, will be to fold.
To this end, here is a higher-order function that, given a transducer function, an input, and a list of configurations, applies the transition function to each configuration and returns the new list of configurations along with the list of outputs that were generated:
transduceAll t input [] acc = acc
transduceAll t input (config:configs) (accConfigs, accOutputs) =
let
(config', outputs) = t config input
newAcc = ((config':accConfigs), accOutputs ++ outputs)
in
transduceAll t input configs newAcc
Since this is a fold, it can also be written using fold, but this too
is left as an exercise for the reader.
Using transduceAll, we can now give an example of a barnTransducer which
manages a list of countingLightTransducers behind a barn door. The TurnOn
and TurnOff inputs will be sent to all of the lights (but only when the barn
door is open), and every light will maintain its own counter and will contribute
to the collective list of outputs as appropriate.
barnTransducer config input =
case (config.mode, input) of
(Closed, Open) ->
({mode:Opened, data:config.data}, [])
(Opened, Close) ->
({mode:Closed, data:config.data}, [])
(Opened, lightInput) ->
let
lightConfigs = config.data
(lightConfigs', lightOutputs) =
transduceAll (countingLightTransducer) lightInput lightConfigs ([], [])
in
({mode:config.mode, data:lightConfigs'}, lightOutputs)
_ ->
(config, [])
In the absence of hierarchially nested state machines, entry and exit actions are unproblematic. If you know that when you leave mode A you must do action X and when you enter mode B you must do action Y, you can simply specify those outputs during that transition.
someTransducer config input =
case (config.mode, input) of
...
(ModeA, InputFoo) ->
let
config' = {mode:ModeB, data:config.data}
in
(config', [ActionX, ActionY])
But in the presence of a hierarchy of states, it becomes more complicated. Since A and B may be nested arbitrarily deeply, we must issue, between the exit actions of A and the entry actions of B, the exit and entry actions of all the containing states that we exit or enter on the way from A to B, in the sequence we would encounter them on that journey through the "nested state tree".
Since such sequences can be determined by examining the state machine's structure, it is possible to automate the production of these sequences, at either code-generation time (e.g. in CASE tools that translate state machines to C/C++), or as the responsibility of a state machine "interpreter" used at run-time.
But if we want to directly write (and compose) functions that describe this behaviour, neither of those options is available to us.
I don't pretend to have an optimal solution for this. I'm not too fussed about that, because having the equivalent of exit and entry actions wasn't one of the four requirements listed in the first section. They would be "nice to have" though, so I would like to show that retaining them is not an insurmountable problem.
What we can do, is define a higher-order function h which takes a transducer t and returns a transducer t′ that transitions in the same way t does, but, under some conditions, produces extra outputs. These extra outputs may occur before or after (or indeed, around) the outputs produced by t.
If this is all that h does, then we can also say that functions like h are well-behaved during composition: if h1 adds extra outputs before t and h2 adds extra outputs after t, their composition adds extra outputs around t.
We can derive an h function from the structure of a state machine, ensuring that h always produces extra outputs that correspond correctly with the entry actions and exit actions defined for each mode in each transducer.
It only remains for all transducers under consideration (even those that are nested within other transducers) to be "wrapped" with this higher-order function h.
Sketch:
addEntryExitOutputs t config input =
let
(config', outputs) = t config input
exitOutputs = case config.mode of
SomeFromMode -> [SomeExitAction]
_ -> []
entryOutputs = case config'.mode of
SomeToMode -> [SomeEntryAction]
_ -> []
outputs' = exitOutputs ++ outputs ++ entryOutputs
in
(config', outputs')
I would like to stress that I think all transducers in the hierarchy need to be "wrapped" with this higher-order function, otherwise there is the potential for some exit or entry action to be missed.
Are there other potential pitfalls from this method?
What if we end up wrapping a transducer in addEntryExitOutputs
multiple times? I am not entirely convinced this would happen
under normal circumstances, but even so, I think one can make a good
argument that exit and entry actions should be idempotent in any case.
In our setting, that could be accomplished by de-duplicating the
produced list of outputs.
More generally, we have to think about "when" the actions represented by any existing outputs of a transducer are intended to take place, relative to any entry and exit actions our wrappers are going to add to the sequence. Any existing output may, upon reflection, strongly resemble an entry or exit action, and if so it should arguably be modelled as one instead.
A more serious departure is that while the UML spec guarantees that a state's exit and entry actions are executed when a transition happens that involves that state, here we can only ever guarantee that a list of exit and entry "action outputs" will be produced; the outer transducer is not strictly required to pass them upward to the "output executor" or otherwise act on them. But I think one can make a good argument that, although outer transducers can "manage" these exit and entry outputs like any other outputs, they generally should not do so. There are probably measures which can be taken to reduce the chance of doing so in error, but an investigation of these possibilities is well beyond the scope of this article.
Often the inputs that a state machine deals with are not "real" inputs at all, but some system's interpretation of what's going on in the world around it. For example, in a GUI, "hover" and "drag" are two different kinds of events, but they are caused by the same physical action (moving the mouse). The only difference is the context in which the physical action occurs.
Synthesized inputs are desirable because they allow the machine to deal with the world outside it at an appropriate level of abstraction. While I'm sure it's possible to define UML state machines that synthesize inputs and respond to synthesized inputs, I'm also not aware of any UML features that are provided for that particular purpose.
In our system of nested transducers, synthesizing inputs is straightforward, as the outer transducer can provide whatever input it likes to the inner transducers that it manages.
A partial example illustrating the synthesizing of a "drag" event in a GUI will probably suffice. (Full, runnable examples can be found in NestedModalTransducers.hs and nested-modal-transducers.scm.)
makeGuiInputSynthesizingTransducer t = t' where
t' config input =
case (config.mode, input.type) of
(MouseDown, MouseMove) ->
t config (Drag input.x input.y)
_ ->
t config input
The outer transducer can do whatever it likes with the outputs produced by an inner transducer that it manages.
As an extreme example of an outer transducer "managing" the outputs of its inner transducers, consider that a transducer which produces an empty list of outputs is indistinguishable from a reducer.
We can always wrap a transducer with an outer transducer that passes its inputs through to the inner transducer unchanged, but takes all of the inner transducer's outputs and does something with them other than return them as its own outputs (it could ignore them, store them in its data, etc.,) resulting in a reducer.
You can go the other way around as well. We can write a
higer-order transducer that, given a reducer, produces outputs
based on the new state of the reducer, every time the reducer
changes state. This would be, in some sense, the functional
equivalent of the subscribe method of a Redux store.
I suppose this suggests some kind of "duality" between transducers and reducers, but I hesitate to characterize it in any more detail than that.
Whenever there are hierarchically nested states, the following situation is possible: the system is in some state in an inner machine, and an input causes it to transition to a different state in the outer machine — one where the inner machine does not even exist. The question is, what happens to the state of that inner machine when that happens? Does it get retained? Or does it get reset to its local start state the next time we enter the outer state that contains it? Or does something else happen?
Statecharts contains some machinery to try to express this situation, notably a concept of "history" of the inner state machine, which is somewhat sophisticated.
Our system of functional transducers does not require any specific machinery for this, because the configuration of the inner transducer is always embedded in the data in the outer transducer. It is essentially retained by default, when the outer transducer switches modes. But the outer transducer also has access to it and could choose to reset it during such a transition if desired. Indeed, a higher-order wrapper such as those that implement entry and exit actions could be used for this purpose, or to implement a more sophisticated scheme for managing this inner configuration, such as "history".
We have described a way to implement UML state machines (or at least, the parts of them that I like) as purely functional transducers. (Footnote 6)
Neither of these ideas are very new, and I have a feeling that someone must have done something like this at some point, but if so, I have not been successful at locating it.
In the interests of brevity, there is much that has been omitted from this article, and consequently, much that could be explored in more detail.
Our emphasis has been on how to construct transducers hierarchically; much existing work on transducers emphasizes hooking the output of one transducer to the input of another (examples include Edward Kmett's Data.Machine), but this sequential style of composition figures not at all into the current work.
In a sense we have given a kind of design pattern for writing functions
with the type S × I → S × [O]. We touched on [S] × I → S × [O]
(in transduceAll) and S × [I] → S × [O] (in rehearse) —
but what other algebraic properties does it have? And what happens when
I = O?
The preponderance of destructuring lets in the code examples here suggests
there might be some higher-order formulation that captures what we're doing —
maybe a monad, possibly even an arrow. On the other hand, the problem
of constructing a state machine (or in our case, a modal transducer)
does not, so far as I've seen, readily lend itself to a combinator approach.
There are none yet. This section will be updated when there are.
A definite next step would be to use nested modal transducer assemblages as the basis for constructing a non-trivial reactive program.
Another definite possibility would be the formulation of a framework for constructing such assemblages, and an investigation into how it could (and whether it should) be presented as a combinator library.
Video games, if you must know.
That's right, I've been trying to formulate a rigorous theory of video games. Do you have a problem with that?
In a reducer, the actions must be composable, i.e. they must form
a monoid. In a transducer, both the inputs must be composable,
and the outputs must be composable. Under reducer combination
(Redux's combineReducers), reducers form a group, but under
transducer combination, transducers form a monoid.
Okay, not real numbers, obviously. You're welcome to extend this model to uncountable sets if you really want of course, it's just that I have no interest in that myself.
"Configuration" is borrowed from modern automata theory, where it is used when describing Turing machines and such. (Older texts sometimes refer to the "instantaneous description" of a machine.) A runner-up for "data" would be "context", which is used by XState, but then both "configuration" and "context" begin with "c", which is poor abbreviatability.
Note also that the term "modal transducer" also refers to a kind of electronic component, but generally I expect this will be disambiguated by context.
And I am finally in a position to present a fully formal treatment of Mr. Do.