An Evaluation of Two Alternatives to Minimax
This addresses game AI for researchers, but appears incremental as it builds on known limitations of minimax.
The paper tackles the problem of move selection in games by evaluating alternatives to the minimax algorithm, finding that new algorithms can use evaluation functions significantly better than minimax.
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.