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.

翻译：摘要：符号机器学习验证器（SMLP）是一种基于对系统进行仿真或执行多个输入向量所获取数据样本的系统探索工具与库。SMLP采用灰盒方法分析系统数据：它结合数据探索的统计方法，与系统响应紧密闭环地构建并探索机器学习模型，并通过融合概率方法与形式化方法对这些模型进行深入分析。目前，SMLP已在英特尔工业环境中应用于模拟级硬件设计的分析与优化。作为通用工具，SMLP适用于可通过采样构建机器学习模型的各类系统。