An Analysis of Forward Pruning

By Stephen J. J. Smith, Dana S. Nau. 1994

How researchers see this paper

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

1. Csaba Szepesvari
2. David Silver
3. Yuxi Li
4. Nan Jiang
5. R´emi Munos
6. Levente Kocsis
7. Matthew Lai
8. Sergey Levine

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.”
• Bandit Based Monte-Carlo Planning (2006, methodology): “A P-game tree [12] is a minimax tree that is meant to model games where at the end of the game the winner is decided by a global evaluation of the board position where some counting method is employed (examples of such games include Go, Amazons and Clobber).”
• Algorithms for computing strategies in two-player simultaneous move games (2016, unknown): “The latter method is based on random P-games [83] and creates more realistic games using the intuition of good and bad moves.”