SMT-based model checking of max-plus linear systems by Mufid, MSU, Micheli, A, Abate, A, Cimatti, A

“…The algorithms are cast in the setting of SMT-based verification of infinite-state transition systems over the reals, with variants depending on the (incremental vs upfront) computation of the bound, and on the (explicit vs implicit) unrolling of the transition relation. …”

Computation of the transient in max-plus linear systems via SMT-solving by Abate, A, Cimatti, A, Micheli, A, Mufid, MS

“…This paper proposes a new approach, grounded in Satisfiability Modulo Theories (SMT), to study the transient of a Max-Plus Linear (MPL) system, that is the number of steps leading to its periodic regime. …”

Abstractions and formal verification of max-plus linear systems by Syifaul Mufid, M

“…Finally, the performance of the proposed methods (abstraction-based and SMT-based) is evaluated via a set of computational benchmarks, where we observe a significant improvement for the SMT-based procedures w.r.t. the scalability, compared to existing approaches whenever available.…”

Learning probabilistic termination proofs by Abate, A, Giacobbe, M, Roy, D

“…We introduce the neural ranking supermartingale: we let a neural network fit an RSM over execution traces and then we verify it over the source code using satisfiability modulo theories (SMT); if the latter step produces a counterexample, we generate from it new sample traces and repeat learning in a counterexample-guided inductive synthesis loop, until the SMT solver confirms the validity of the RSM. …”

Formal synthesis of Lyapunov neural networks by Abate, A, Ahmed, D, Giacobbe, M, Peruffo, A

“…The learner trains a neural network that satisfies the Lyapunov criteria for asymptotic stability over a samples set; the verifier proves via SMT solving that the criteria are satisfied over the whole domain or augments the samples set with counterexamples. …”

Synthesis without syntactic templates by Polgreen, E

“…The first of these improvements is a significant change to the way we encode the synthesis problem for the SAT-based synthesis component of CEGIS in order to produce smaller formulae that can be more efficiently solved by SAT and SMT solvers. The second of these improvements is the use of incremental satisfiability solving in the synthesis component of CEGIS. …”