ML Anthology

An Evaluation of Two Alternatives to Minimax

Dana S. Nau, Paul Walton Purdom Jr., Chun-Hung Tzeng
UAI 1985 pp. 505-509
doi:10.1016/B978-0-444-70058-2.50042-5 /uai/1985/nau1985uai-evaluation/

Abstract

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.

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Text

Nau et al. "An Evaluation of Two Alternatives to Minimax." Conference on Uncertainty in Artificial Intelligence, 1985. doi:10.1016/B978-0-444-70058-2.50042-5

Markdown

[Nau et al. "An Evaluation of Two Alternatives to Minimax." Conference on Uncertainty in Artificial Intelligence, 1985.](https://mlanthology.org/uai/1985/nau1985uai-evaluation/) doi:10.1016/B978-0-444-70058-2.50042-5

BibTeX

@inproceedings{nau1985uai-evaluation,
  title     = {{An Evaluation of Two Alternatives to Minimax}},
  author    = {Nau, Dana S. and Jr., Paul Walton Purdom and Tzeng, Chun-Hung},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1985},
  pages     = {505-509},
  doi       = {10.1016/B978-0-444-70058-2.50042-5},
  url       = {https://mlanthology.org/uai/1985/nau1985uai-evaluation/}
}