Silas: A high-performance machine learning foundation for logical reasoning and verification

Hadrien Bride, Cheng Hao Cai, Jie Dong, Jin Song Dong, Zhé Hóu, Seyedali Mirjalili, Jing Sun

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions.

Original languageEnglish
Article number114806
JournalExpert Systems with Applications
Publication statusPublished - 15 Aug 2021


  • Ensemble trees
  • Explainable artificial intelligence
  • High-performance machine learning
  • Logical reasoning


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