---
title: "Search Versus Knowledge in Game-Playing Programs Revisited"
authors:
  - "Andreas Junghanns"
  - "Jonathan Schaeffer"
doi: ""
url: "https://ijcai.org/proceedings/1997/101"
pdf: "https://ijcai.org/proceedings/1997/0101.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "692-696"
track: "Game Playing"
keywords:
---

# Search Versus Knowledge in Game-Playing Programs Revisited

Andreas Junghanns, Jonathan Schaeffer

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 692-696.

## 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-knowledge tradeoff.