  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.
