https://www.proquest.com/openview/2a5f2e00e8df7ea3f1fd3e8619...
A few of my own experiments in this time with unification over the binders as variables themselves shows there’s almost always a post HM inference sitting there but likely not one that works in total generality.
To me that spot of trying to binding unification in higher order logic constraint equations is the most challenging and interesting problem since it’s almost always decidable or decidably undecidable in specific instances, but provably undecidable in general.
So what gives? Where is this boundary and does it give a clue to bigger gains in higher order unification? Is a more topological approach sitting just behind the veil for a much wider class of higher order inference?
And what of optimal sharing in the presence of backtracking? Lampings algorithm when the unification variables is in the binder has to have purely binding attached path contexts like closures. How does that get shared?
Fun to poke at, maybe just enough modern interest in logic programming to get there too…
[1]: https://www.lix.polytechnique.fr/~dale/lProlog/proghol/extra...
Second you really need to understand and fine tune cuts, and other search optimization primitives.
Finally in what concerns Game AIs, it is a mixture of algorithms and heuristics, a single paradigm language (first order logic) like Prolog, can't be a tool for all nails.
I know you likely mean regular Prolog, but that's actually fairly easy and intuitive to reason with (code dependent). Lambda Prolog is much, much harder to reason about IMO and there's a certain intractability to it because of just how complex the language is.