@conference {18875,
	title = {Game-tree search with combinatorially large belief states},
	volume = {19},
	year = {2005},
	month = {2005///},
	pages = {254 - 254},
	abstract = {In games such as kriegspiel chess (a chess variantwhere players have no direct knowledge of the op-
ponent{\textquoteright}s pieces{\textquoteright} locations) the belief state{\textquoteright}s sizes
dwarf those of other partial information games like
bridge, scrabble, and poker{\textendash}and there is no easy
way to generate states satisfying the given observa-
tions. We show that statistical sampling approaches
can be developed to do well in such games.
We show that it is not necessary for the random
sample to consist only of game boards that satisfy
each and every one of a player{\textquoteright}s observations. In
fact, we win 24\% more often by beginning with
such completely consistent boards and gradually
switching (as the game progressed) to boards that
are merely consistent with the latest observation.
This surprising result is explained by noting that as
the game progresses, a board that is consistent with
the last move becomes more and more likely to be
consistent with the entire set of observations, even
if we have no idea what sequence of moves might
have actually generated this board.
},
	url = {http://www.ijcai.org/papers/0878.pdf},
	author = {Parker,A. and Nau, Dana S. and V.S. Subrahmanian}
}
