129 citations · 153 across the 17 of their papers we have counts for
9 papers · 1 filter
Complete -Regular Supermartingale Certificates
Alessandro Abate, Mirco Giacobbe, Sergey Ichtchenko +1
We introduce a general methodology for the construction of sound and complete proof rules for the almost-sure and quantitative acceptance of reactivity properties on time-homogeneo…
Branching Bisimulation Learning
Alessandro Abate, Mirco Giacobbe, Christian Micheletti +1
We introduce a bisimulation learning algorithm for non-deterministic transition systems. We generalise bisimulation learning to systems with bounded branching and extend its applic…
Quantitative Supermartingale Certificates
Alessandro Abate, Mirco Giacobbe, Diptarko Roy
We introduce a general methodology for quantitative model checking and control synthesis with supermartingale certificates. We show that every specification that is invariant to ti…
Neural Model Checking
Mirco Giacobbe, Daniel Kroening, Abhinandan Pal +1
We introduce a machine learning approach to model checking temporal logic, with application to formal hardware verification. Model checking answers the question of whether every ex…
Stochastic Omega-Regular Verification and Control with Supermartingales
Alessandro Abate, Mirco Giacobbe, Diptarko Roy
We present for the first time a supermartingale certificate for -regular specifications. We leverage the Robbins & Siegmund convergence theorem to characterize supermartingale c…
Bisimulation Learning
Alessandro Abate, Mirco Giacobbe, Yannik Schnitzer
We introduce a data-driven approach to computing finite bisimulations for state transition systems with very large, possibly infinite state space. Our novel technique computes stut…
On the Trade-off Between Efficiency and Precision of Neural Abstraction
Alec Edwards, Mirco Giacobbe, Alessandro Abate
Neural abstractions have been recently introduced as formal approximations of complex, nonlinear dynamical models. They comprise a neural ODE and a certified upper bound on the err…
Neural Abstractions
Alessandro Abate, Alec Edwards, Mirco Giacobbe
We present a novel method for the safety verification of nonlinear dynamical models that uses neural networks to represent abstractions of their dynamics. Neural networks have exte…
Quantitative Verification with Neural Networks
Alessandro Abate, Alec Edwards, Mirco Giacobbe +2
We present a data-driven approach to the quantitative verification of probabilistic programs and stochastic dynamical models. Our approach leverages neural networks to compute tigh…