Finding Optimal Strategies for Imperfect Information Games
Ian Frank, David Basin, Hitoshi Matsubara
Presented at the Fifteenth
National Conference on Artificial Intelligence, AAAI-98.
Please note that the AAAI copyright agreement allows authors (but only
authors) to post their papers on a Web or ftp site.
Abstract:
We examine three heuristic algorithms for games with imperfect
information: Monte-carlo sampling, and two new algorithms we call
vector minimaxing and payoff-reduction minimaxing. We
compare these algorithms theoretically and experimentally, using
both simple game trees and a large database of problems from the
game of Bridge. Our experiments show that the new algorithms both
out-perform Monte-carlo sampling, with the superiority
of payoff-reduction minimaxing being especially marked.
On the Bridge problem set, for example, Monte-carlo sampling only
solves 66% of the problems, whereas payoff-reduction minimaxing
solves over 95%. This level of performance was even good enough to
allow us to discover five errors in the expert text used to generate
the test database.
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