Inductive Logic Progamming (ILP) is a class of machine learning algorithms that learn logic programs from examples and background knowledge (where background knowledge is a logic theory, i.e. another logic program, as ar the examples).
SAT is the boolean satisfiability problem, the problem of finding variable assignments to the variables of a boolean formula that make the formula true.
So the two are not similar and one is not an instance of the other.
The δILP paper describes a differentiable ILP system that learns logic programs with a differentiable logic representation.
Finally, the paper above (SATNet) describes a differentiable satisfiabiilty solver that can be incorporated in a neural network architecture to enable the nerual net to solve satisfiability problems and perform reasoning.
Looks like they (SATNet) are not incorporating a real (exact) SAT solver but their differentiable "SAT solver" is a MAXSAT estimator which gives an approximation of the maximum number of clauses that can be made true.
Inductive Logic Progamming (ILP) is a class of machine learning algorithms that learn logic programs from examples and background knowledge (where background knowledge is a logic theory, i.e. another logic program, as ar the examples).
SAT is the boolean satisfiability problem, the problem of finding variable assignments to the variables of a boolean formula that make the formula true.
So the two are not similar and one is not an instance of the other.
The δILP paper describes a differentiable ILP system that learns logic programs with a differentiable logic representation.
Finally, the paper above (SATNet) describes a differentiable satisfiabiilty solver that can be incorporated in a neural network architecture to enable the nerual net to solve satisfiability problems and perform reasoning.