There are also Constraint Programming solvers (some SAT based, some not) and (Mixed) Integer Programming solvers (not SAT based).
Each "school" excels at different types of problems. ASP for modelling a knowledge-base and running queries on it, CP for discrete optimization problems or for all-solution search, SMT for formal verification and proofs, MIP for optimization of (mostly) continuous variables.
Modern solvers in these "schools" can do things traditionally meant for other "schools". Z3 can do optimization, clingo can include CP-style constraints with clingcon, some MIP solvers can find all solutions, etc.
All in all, this type of "classical" AI is super interesting and I hope the hype on LLMs doesn't suck up all the funding that would go to research in this area.
Each "school" excels at different types of problems. ASP for modelling a knowledge-base and running queries on it, CP for discrete optimization problems or for all-solution search, SMT for formal verification and proofs, MIP for optimization of (mostly) continuous variables.
Modern solvers in these "schools" can do things traditionally meant for other "schools". Z3 can do optimization, clingo can include CP-style constraints with clingcon, some MIP solvers can find all solutions, etc.
All in all, this type of "classical" AI is super interesting and I hope the hype on LLMs doesn't suck up all the funding that would go to research in this area.