These systems are discrete and their state changes are rule based. Example:
Given a chess position, generate a series of moves that will lead to it (there may be many, one, or none, but I only need one valid one)
If there are many examples, (end state, state change sequence) pairs, is there a neural network or other machine learning algorithm that can generate the latter given the former in a way that extrapolates to previously unseen board positions?
In my use cases, it will be possible to find or generate large datasets for both.