Tsetlin machine


A Tsetlin machine is an artificial intelligence algorithm based on propositional logic.

Background

A Tsetlin machine is a form of learning automaton collective for learning patterns using propositional logic. Ole-Christoffer Granmo created and gave the method its name after Michael Lvovitch Tsetlin, who invented the Tsetlin automaton and worked on Tsetlin automata collectives and games. Collectives of Tsetlin automata were originally constructed, implemented, and studied theoretically by Vadim Stefanuk in 1962.
The Tsetlin machine uses computationally simpler and more efficient primitives compared to more ordinary artificial neural networks.
As of April 2018 it has shown promising results on a number of test sets.

Types

  • Original Tsetlin machine
  • Convolutional Tsetlin machine
  • Regression Tsetlin machine
  • Relational Tsetlin machine
  • Weighted Tsetlin machine
  • Arbitrarily deterministic Tsetlin machine
  • Parallel asynchronous Tsetlin machine
  • Coalesced multi-output Tsetlin machine
  • Tsetlin machine for contextual bandit problems
  • Tsetlin machine autoencoder
  • Tsetlin machine composites: plug-and-play collaboration between specialized Tsetlin machines
  • Contracting Tsetlin machine with absorbing automata
  • Graph Tsetlin machine
  • Fuzzy-Pattern Tsetlin Machine

Applications

Original Tsetlin machine

DescriptionSymbol
Number of binary inputs
Number of classes
Number of clauses per class
Number of automaton states
Automaton decision boundary
Automaton initialization state
Feedback threshold
Learning sensitivity

Tsetlin automaton

The Tsetlin automaton is the fundamental learning unit of the Tsetlin machine. It tackles the multi-armed bandit problem, learning the optimal action in an environment from penalties and rewards. Computationally, it can be seen as a finite-state machine that changes its states based on the inputs. The FSM will generate its outputs based on the current states.
  • A quintuple describes a two-action Tsetlin automaton:
  • A Tsetlin automaton has states, here :
  • The FSM can be triggered by two input events
  • The rules of state migration of the FSM are stated as
  • It includes two output actions
  • Which can be generated by the algorithm

Boolean input

A basic Tsetlin machine takes a vector of Boolean features as input, to be classified into one of two classes, or. Together with their negated counterparts,, the features form a literal set.

Clause computing module

A Tsetlin machine pattern is formulated as a conjunctive clause, formed by ANDing a subset of the literal set:
For example, the clause consists of the literals and outputs iff and .

Summation and thresholding module

The number of clauses employed is a user-configurable parameter. Half of the clauses are assigned positive polarity. The other half is assigned negative polarity. The clause outputs, in turn, are combined into a classification decision through summation and thresholding using the unit step function :
In other words, classification is based on a majority vote, with the positive clauses voting for and the negative for. The classifier
for instance, captures the XOR-relation.

Feedback module

Resource allocation

Resource allocation dynamics ensure that clauses distribute themselves across the frequent patterns, rather than missing some and overconcentrating on others. That is, for any input, the probability of reinforcing a clause gradually drops to zero as the clause output sum
approaches a user-set target for .
If a clause is not reinforced, it does not give feedback to its Tsetlin automata, and these are thus left unchanged. In the extreme, when the voting sum equals or exceeds the target, no clauses are reinforced. Accordingly, they are free to learn new patterns, naturally balancing the pattern representation resources.

Implementations

Software

Hardware

  • One of the first FPGA-based hardware implementation of the Tsetlin Machine on the Iris flower data set was developed by the μSystems Research Group at Newcastle University.
  • They also presented the first ASIC implementation of the Tsetlin Machine focusing on energy frugality, claiming it could deliver 10 trillion operation per Joule. The ASIC design had demoed on DATA2020.

Books

Conferences

  • International Symposium on the Tsetlin Machine

Videos

Papers

Publications/news/articles