Tsetlin machine

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

A Tsetlin machine is a form of learning automaton collective for learning patterns using propositional logic.

Ole-Christoffer Granmo created[1] and gave the method its name after Michael Lvovitch Tsetlin, who invented the Tsetlin automaton[2] and worked on Tsetlin automata collectives and games.

[3] 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.

[4] As of April 2018 it has shown promising results on a number of test sets.

[5][6] 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 (FSM) that changes its states based on the inputs.

The FSM will generate its outputs based on the current states.

{\displaystyle F(\phi _{u},\beta _{v})={\begin{cases}\phi _{u+1},&{\text{if}}~1\leq u\leq 3~{\text{and}}~v={\text{Penalty}}\\\phi _{u-1},&{\text{if}}~4\leq u\leq 6~{\text{and}}~v={\text{Penalty}}\\\phi _{u-1},&{\text{if}}~1

A basic Tsetlin machine takes a vector

of o Boolean features as input, to be classified into one of two classes,

, the features form a literal set

A Tsetlin machine pattern is formulated as a conjunctive clause

, formed by ANDing a subset

of the literal set:

consists of the literals

and outputs 1 iff

The number of clauses employed is a user-configurable parameter n. 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

, for instance, captures the XOR-relation.

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 X, the probability of reinforcing a clause gradually drops to zero as the clause output sum

approaches a user-set target T 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 v equals or exceeds the target T (the Tsetlin Machine has successfully recognized the input X), no clauses are reinforced.

Accordingly, they are free to learn new patterns, naturally balancing the pattern representation resources.