@conference {17786,
	title = {Overconfidence or paranoia? search in imperfect-information games},
	booktitle = {PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE},
	volume = {21},
	year = {2006},
	month = {2006///},
	pages = {1045 - 1045},
	abstract = {We derive a recursive formula for expected utility valuesin imperfect- information game trees, and an imperfect-
information game tree search algorithm based on it. The for-
mula and algorithm are general enough to incorporate a wide
variety of opponent models. We analyze two opponent mod-
els. The {\textquotedblleft}paranoid{\textquotedblright} model is an information-set analog of the
minimax rule used in perfect-information games. The {\textquotedblleft}over-
confident{\textquotedblright} model assumes the opponent moves randomly.
Our experimental tests in the game of kriegspiel chess (an
imperfect-information variant of chess) produced surpris-
ing results: (1) against each other, and against one of the
kriegspiel algorithms presented at IJCAI-05, the overconfi-
dent model usually outperformed the paranoid model; (2) the
performance of both models depended greatly on how well
the model corresponded to the opponent{\textquoteright}s behavior. These re-
sults suggest that the usual assumption of perfect-information
game tree search{\textemdash}that the opponent will choose the best pos-
sible move{\textemdash}isn{\textquoteright}t as useful in imperfect-information games.
},
	url = {http://www.aaai.org/Papers/AAAI/2006/AAAI06-164.pdf},
	author = {Parker,A. and Nau, Dana S. and V.S. Subrahmanian}
}
