SMLP: Symbolic Machine Learning Prover
Franz Brauße Zurab Khasidashvili Konstantin Korovin
Symbolic Machine Learning Prover (SMLP) is a tool and a library for system
exploration based on data samples obtained by simulating or executing the
system on a number of input vectors. SMLP aims at exploring the system based on
this data by taking a grey-box approach: SMLP combines statistical methods of
data exploration with building and exploring machine learning models in close
feedback loop with the system's response, and exploring these models by
combining probabilistic and formal methods. SMLP has been applied in industrial
setting at Intel for analyzing and optimizing hardware designs at the analog
level. SMLP is a general purpose tool and can be applied to systems that can be
sampled and modeled by machine learning models.