Search Versus Knowledge in Game-Playing Programs Revisited
Abstract
Perfect knowledge about a domain renders search unnecessary and, likewise, exhaustive search obviates heuristic knowledge. In practise, a tradeoff is found somewhere in the middle, since neither extreme is feasible for interesting domains. During the last two decades, the focus for increasing the performance of two-player game-playing programs has been on enhanced search, usually by faster hardware and/or more efficient algorithms. This paper revisits the issue of the relative advantages of improved search and knowledge. It introduces a revised search-knowledge tradeoff graph that is supported by experimental evidence for three different games: chess, Othello and checkers, using a new metric: the "noisy oracle". Previously published results in chess seem to contradict our model, postulating a linear increase in program strength with increasing search depth. We show that these results are misleading, and are due to properties of chess and chess-playing programs, not to the search-knowle...
Cite
Text
Junghanns and Schaeffer. "Search Versus Knowledge in Game-Playing Programs Revisited." International Joint Conference on Artificial Intelligence, 1997.Markdown
[Junghanns and Schaeffer. "Search Versus Knowledge in Game-Playing Programs Revisited." International Joint Conference on Artificial Intelligence, 1997.](https://mlanthology.org/ijcai/1997/junghanns1997ijcai-search/)BibTeX
@inproceedings{junghanns1997ijcai-search,
title = {{Search Versus Knowledge in Game-Playing Programs Revisited}},
author = {Junghanns, Andreas and Schaeffer, Jonathan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1997},
pages = {692-697},
url = {https://mlanthology.org/ijcai/1997/junghanns1997ijcai-search/}
}