I discovered the above implementation about the time I finished reading through the paper. I need to go back through and finish taking notes.
I think there might be room to add `any` and `all` guards that take many guards as arguments.
The other thing I've been pondering is the apparent similarity between ASTs and the sort of graph a statechart forms. If we compress state and chart into a single object, now you have states that can have parents and children (the semantics of what is an active state would vary with your node type, IE compound or parallel). Then your edges would the transitions between nodes.
The semantics of the whole thing starts to look vaguely like there's a programming language that could emerge if the intermediate representation is hammered out well enough.
At Amazon Robotics we built a robot control system that executed state charts (described in an extended version of SCXML) in real-time for managing the core behaviors of the robot.
To help people not have write the charts by hand, we built a DSL (originally in python, then in Kotlin), to author robot behaviors that then compiled down to SCXML. Conditions on guards were written in a tiny expression language we wrote, enabling you to look at various signals from the hardware and software at runtime and make decisions based off them.
The nice part of this setup was that it opened up a path for doing more formal analysis on the behavior. E.g. you could easily find "terminal" states that you could enter but not leave. You can also imagine things like checking things like which states in a parallel or in concurrently running machines, aren't allowed to be active, and verifying there is no path in the state graph that allowed that to occur. There were other nice properties as well, like getting a graphical visualization of your program state "for free"
It was definitely a more constrained model than a "full featured" programming language, but for our use case, controlling machines in a reliable way, it worked out very well!