Behavior Classification for Turing Machines
Nestor Diaz
Faculty of Electronics and Telecommunications Engineering
Universidad del Cauca, Popayán, Colombia
and
Faculty of Engineering
Systems Engineering and Computation School
Universidad del Valle, Cali, Colombia
Abstract
A classification for Turing machines is described. Quantitative descriptors for Turing machine behavior are used for measuring repetitiveness, periodicity, complexity and entropy. These descriptors allowed identifying several kinds of behavior for Turing machines, using an approach based on machine learning. The classification was tested and generality was probed over different configurations of Turing machines.
