In this section, three game-playing programs that use simulations
are described. These programs do not use Monte Carlo
sampling to generate instances of the missing information. They use
variations of selective sampling; sampling biased towards taking advantage of
all the available information.
They use information about the game state to skew the underlying probability
distribution of the opponents' moves, cards or tiles, rather than
assuming uniform or other fixed probability distributions.
The general simulation algorithm used by
these games to select a move from a set *M* of candidate moves is:

- 1.
- Construct a set
*I*of instances of the missing information consistent with the public information about the state of the game and the program's assumptions (information) about the opponents. - 2.
- For each move
and each instance ,
evaluate the
result of making the move
*m*in the instance*i*. Denote the score obtained by making this move*s*(*m*,*i*). - 3.
- Return that
*m*for which is maximal.