The breakthrough algorithm combining Tsetlin automata with propositional logic was originally published in 2018 by Ole-Christoffer Granmo, a professor from University of Agder, Norway, and advisor at Literal Labs. Its operation was initially demonstrated in image recognition by constructing propositions (known as clauses) in logic, based on literals and configuration connections controlled by Tsetlin automata.

The combination of the Tsetlin automata and the propositional logic gives rise to a very efficient (energy and performance) computational model for ML. This process can be used to model complex behaviour of systems in the form of teams or collectives of automata, allowing us to reach the optimal decisions in complex systems with greater reliability and redundancy, and operates on the principles of statistical optimality, physical distribution in space and alleviate the criticalities and anomalies. 

The training of Tsetlin automata is achieved through the process of evolution of each automaton through its states, forming a linear sequence. Each state represents the level of confidence of the automaton in performing its actions. The actions of the automaton are associated with two subsets of states, one for an action switching a connection between an input literal and a clause ON and the other for switching it OFF.  When the states are organised in a linear sequence we can control this level of confidence by applying simple transitions between the states, thereby either rewarding or penalising the automaton’s actions. These actions are somewhat similar to weights in NNs. However, unlike complex multiplication-based weights, the Tsetlin automata “weights” are simple logic signals that control the configuration of input literals in the clauses.

Yakovlev and Shafik, and their team at Newcastle, have been working on hardware and software implementation of Tsetlin Machines adding new data representation techniques (e.g. Booleanisation), parallelisation and compression methods based on indexing input literals, tiled architectures, and hardware-software codesign of ML learning systems. These combinations of techniques amplify the TM advantages by orders of magnitude, including up to 1,000X faster inferencing and orders of magnitude more energy savings than NNs. They also brought a new level of understanding of the dynamics of machine learning processes by visualising the learning processes and identifying important analytical characteristics of hyperparameters of TMs, such as thresholds on clause voting and feedback activation.