I don't understand how the star1 variation mentioned in the paper is Sparse Sampling for Adversarial different than the usual star1 algorithm?(Taken from the bottom of page 7). This variant can be easily  described in terms of the descriptions of Star1. To enable sampling,  one need only change the implementation of getOutcomeSet on line 5 of  Algorithm 1. Instead of generating the full list of moves, the new  function samples c outcomes with replacement and assigns a uniform  distribution over the new outcome set of size c.Does the variation only generate some of the possible game states and not all of them? I think I understand star1 but I don't really understand star2 because it is more complicated. I only have a bachelor's degree in computers and this theoretical stuff is way above my head. Thanks for any help that I get. :)
How does this star1 variation work? (star1 is a stochastic (dice) algorithm for artificial intelligence in adversarial games)
artificial intelligence;board games