  A drawback of Kolmogorov-Chaitin complexity (K) as a function from s to the
shortest program producing s is its noncomputability which limits its range of
applicability. Moreover, when strings are short, the dependence of K on a
particular universal Turing machine U can be arbitrary. In practice one can
approximate it by computable compression methods. However, such compression
methods do not always provide meaningful approximations--for strings shorter,
for example, than typical compiler lengths. In this paper we suggest an
empirical approach to overcome this difficulty and to obtain a stable
definition of the Kolmogorov-Chaitin complexity for short sequences.
Additionally, a correlation in terms of distribution frequencies was found
across the output of two models of abstract machines, namely unidimensional
cellular automata and deterministic Turing machine.
