  The basis of the method proposed in this article is the idea that information
is one of the most important factors in strategic decisions, including
decisions in computer chess and other strategy games. The model proposed in
this article and the algorithm described are based on the idea of a information
theoretic basis of decision in strategy games . The model generalizes and
provides a mathematical justification for one of the most popular search
algorithms used in leading computer chess programs, the fractional ply scheme.
However, despite its success in leading computer chess applications, until now
few has been published about this method. The article creates a fundamental
basis for this method in the axioms of information theory, then derives the
principles used in programming the search and describes mathematically the form
of the coefficients. One of the most important parameters of the fractional ply
search is derived from fundamental principles. Until now this coefficient has
been usually handcrafted or determined from intuitive elements or data mining.
There is a deep, information theoretical justification for such a parameter. In
one way the method proposed is a generalization of previous methods. More
important, it shows why the fractional depth ply scheme is so powerful. It is
because the algorithm navigates along the lines where the highest information
gain is possible. A working and original implementation has been written and
tested for this algorithm and is provided in the appendix. The article is
essentially self-contained and gives proper background knowledge and
references. The assumptions are intuitive and in the direction expected and
described intuitively by great champions of chess.
