Silas: A high-performance machine learning foundation for logical reasoning and verification. (15th August 2021)

• Record Type: Journal Article
• Authors: Bride, Hadrien; Cai, Cheng-Hao; Dong, Jie; Dong, Jin Song; Hóu, Zhé; Mirjalili, Seyedali; Sun, Jing
• Is Part Of: Expert systems with applications. Volume 176(2021)
• Publication Date: 2021-08-15
• Subjects: High-performance machine learning -- Ensemble trees -- Explainable artificial intelligence -- Logical reasoning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114806
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: High-performance computing speeds up decision trees while consuming less memory. Ensembled decision trees are explainable with a maximum satisfiability solver. Ensembled decision trees can be verified against user specifications. Abstract: 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.