  A popular current research trend deals with expanding the Monte-Carlo tree
search sampling methodologies to the environments with uncertainty and
incomplete information. Recently a finite population version of Geiringer
theorem with nonhomologous recombination has been adopted to the setting of
Monte-Carlo tree search to cope with randomness and incomplete information by
exploiting the entrinsic similarities within the state space of the problem.
The only limitation of the new theorem is that the similarity relation was
assumed to be an equivalence relation on the set of states. In the current
paper we lift this "curtain of limitation" by allowing the similarity relation
to be modeled in terms of an arbitrary set cover of the set of state-action
pairs.
