[GUEST] Aaron Bembenek: From Datalog Programs to SMT Theories [2021-12-08]
Channel: Cypress at Stevens
Date: 08/12/2021

SMT solving and Datalog are two popular automated reasoning technologies: SMT solving extends SAT solving with theories that makes it possible to reason about entities like arrays and integers; Datalog is a simple (but surprisingly useful) logic programming language. This talk explores an interesting connection between the two: every Datalog program can be automatically transformed into an efficient SMT theory. This means that it should be possible to take a Datalog program, plug it into an amenable SMT solver, and then efficiently solve SMT formulas that refer to predicates defined in the Datalog program. I will explain the theory behind this, discuss our prototype implementation built into CVC4, and propose some possible applications, ranging from easily prototyping novel SMT theories to synthesizing Datalog programs.

Aaron Bembenek is a PhD student at Harvard, advised by Steve Chong.
https://people.seas.harvard.edu/~bembenek/

This guest talk was given at the Cypress meeting on Wednesday, December 8th, 2021.