Experiments with the Null-Move Heuristic

By G. Goetsch, M. Campbell. 1990

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Matthew Lai
2. Jonathan Schaeffer
3. Duane Szafron
4. David Silver
5. Murray Campbell
6. Paul Lu
7. A. Joseph Hoane Jr.
8. Brent Knight

Citations in the corpus

• Giraffe: Using Deep Reinforcement Learning to Play Chess (2015, background): “While modern chess engines have improved on that by introducing various types of extensions and reductions [36] [37] [38], the fundamental idea of using depth to define search trees has not changed.”
• Deep Blue (2002, background): “, null move pruning [3,9]) did not provide us enough evidence to warrant implementation in the hardware search of Deep Thought 2 or Deep Blue.”
• A World Championship Caliber Checkers Program (1992, background): “Null-move searches [3, 12] are useful in chess for achieving large reductions in the search trees, but cannot be reliably used by checkers programs because zugzwang is an important aspect of all stages of the game.”