It's not difficult to reduce scheduling problems that are usually done with dynamic programming etc. to SAT.
Then there's SATPLAN. If I had VC money I would be seeking young'uns trying to change the world with SATPLAN, of course in a way that's married to machine learning. (Machine learning doesn't "think", it "learns"; logic-based AI "thinks" but can't "learn").
E: part 1 says at the outset:
> As an example, the often-talked-about dependency management problem, is also NP-Complete and thus translates into SAT[2][3], and SAT could be translated into dependency manager. The problem our group worked on, generating key and lock cuttings based on user-provided lock-chart and manufacturer-specified geometry, is also NP-complete.<
Part 3 is already deep in the rabbit hole of comparing heuristics. People like me just write something that "compiles" to DIMACS and fires Minisat.
A primary problem in marrying logic to ML is doing so in a differentiable way. i.e. when your SAT part of the system is suboptimal, how do you know which parameters of the learned part to change, and in which direction?
We propose Differentiable Satisfiability and Differentiable Answer Set Programming (Differentiable SAT/ASP) for multi-model optimization. Models (answer sets or satisfying truth assignments) are sampled using a novel SAT/ASP solving approach which uses a gradient descent-based branching mechanism.
From the article, a lot of the heuristics seems to revolve around and 1) which variables to try first 2) which learned clauses to keep in your set.
Assign scores and differentiate on those scores. This is (very loosely) how AlphaGo also works - you take a tree-based algorithm (MCTS) and then use neural nets to score the next node to expand.
Then there's SATPLAN. If I had VC money I would be seeking young'uns trying to change the world with SATPLAN, of course in a way that's married to machine learning. (Machine learning doesn't "think", it "learns"; logic-based AI "thinks" but can't "learn").
E: part 1 says at the outset:
> As an example, the often-talked-about dependency management problem, is also NP-Complete and thus translates into SAT[2][3], and SAT could be translated into dependency manager. The problem our group worked on, generating key and lock cuttings based on user-provided lock-chart and manufacturer-specified geometry, is also NP-complete.<
Part 3 is already deep in the rabbit hole of comparing heuristics. People like me just write something that "compiles" to DIMACS and fires Minisat.