An Evaluation of Two Alternatives to Minimax
Dana Nau, Paul Purdom, Chun-Hung Tzeng
In the field of Artificial Intelligence, traditional approaches to choosing moves in games involve the we of the minimax algorithm. However, recent research results indicate that minimizing may not always be the best approach. In this paper we summarize the results of some measurements on several model games with several different evaluation functions. These measurements, which are presented in detail in [NPT], show that there are some new algorithms that can make significantly better use of evaluation function values than the minimax algorithm does.
Keywords: Minimax Algorithm, Minimax Alternatives
PDF Link: /papers/85/p505-nau.pdf
AUTHOR = "Dana Nau
and Paul Purdom and Chun-Hung Tzeng",
TITLE = "An Evaluation of Two Alternatives to Minimax",
BOOKTITLE = "Uncertainty in Artificial Intelligence Annual Conference on Uncertainty in Artificial Intelligence (UAI-85)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1985",
PAGES = "505--509"