To solve many problems modeled as Constraint Satisfaction Problems there are no known efficient algorithms. The specialized literature offers a variety of solvers, which have shown good performance. Nevertheless, despite the efforts of the scientific community in developing new strategies, there is no algorithm that is the best for all possible situations. This paper analyses recent developments of Autonomous Search Constraint Solving Systems. Showing that the design of the most efficient and recent solvers is very close to the Experiential Learning Cycle from organizational psychology.