Logic-Based Machine Learning with Reproducible Decision Model Using the Tsetlin Machine

Tsetlin Machine (TM) is a recent automaton-based algorithm for reinforcement learning. It has demonstrated competitive accuracy on many popular benchmarks while providing a natural interpretability. Due to its logically underpinning it is amenable to hardware implementation with faster performance and higher energy efficiency than conventional Artificial Neural Networks (ANNs). This paper provides an overview of Tsetlin Machine architecture and its hyper-parameters as compared to ANN. Furthermore, it gives practical examples of TM application for patterns recognition using MNIST dataset as a case study. In this work we also prove reproducibility of TM learning process to confirm its trustworthiness and convergence in the light of the stochastic nature of TAs reinforcement.