A Tselin automata returns always true or always false depending on its internal state (wich is basically a counter set during the learning process).
Using one automata per literal per clause you can learn any logical formula in normalized form (the automata tells you if you should include this literal or not).
That section made the paper worth reading for me.
A Tselin machine is a refinement of the formula learning algorithm to get better learning properties, once trained you get a (long) formula (not precisely a logical formula since it uses a sum and a threshold instead of OR to improve robustness).
It is important to note that they manipulate boolean inputs and outputs and, thus, it is not a general alternativ for neural network.
Using one automata per literal per clause you can learn any logical formula in normalized form (the automata tells you if you should include this literal or not). That section made the paper worth reading for me.
A Tselin machine is a refinement of the formula learning algorithm to get better learning properties, once trained you get a (long) formula (not precisely a logical formula since it uses a sum and a threshold instead of OR to improve robustness).
It is important to note that they manipulate boolean inputs and outputs and, thus, it is not a general alternativ for neural network.