first-order-logic

This paper explores the expressive power of Deep Homomorphism Networks (DHNs) for learning over relational databases, linking them to fragments of first-order logic and SQL, and analyzing static analysis problems like emptiness and subsumption.

This paper analyzes the size complexity and decidability of first-order progression in the Situation Calculus, showing that for local-effect, normal, and acyclic actions, progression grows polynomially and remains within decidable fragments such as two-variable first-order logic.