197 citations · 213 across the 6 of their papers we have counts for
6 papers
A Minimax Algorithm Better Than Alpha-beta?: No and Yes
Aske Plaat, Jonathan Schaeffer, Wim Pijls +1
This paper has three main contributions to our understanding of fixed-depth minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on Alpha-Beta, is presented.…
Best-First and Depth-First Minimax Search in Practice
Aske Plaat, Jonathan Schaeffer, Wim Pijls +1
Most practitioners use a variant of the Alpha-Beta algorithm, a simple depth-first pro- cedure, for searching minimax trees. SSS*, with its best-first search strategy, reportedly o…
Nearly Optimal Minimax Tree Search?
Aske Plaat, Jonathan Schaeffer, Wim Pijls +1
Knuth and Moore presented a theoretical lower bound on the number of leaves that any fixed-depth minimax tree-search algorithm traversing a uniform tree must explore, the so-called…
SSS* = Alpha-Beta + TT
Aske Plaat, Jonathan Schaeffer, Wim Pijls +1
In 1979 Stockman introduced the SSS* minimax search algorithm that domi- nates Alpha-Beta in the number of leaf nodes expanded. Further investigation of the algorithm showed that i…
A New Paradigm for Minimax Search
Aske Plaat, Jonathan Schaeffer, Wim Pijls +1
This paper introduces a new paradigm for minimax game-tree search algo- rithms. MT is a memory-enhanced version of Pearls Test procedure. By changing the way MT is called, a number…
Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators
A. Botea, M. Enzenberger, M. Mueller +1
Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discoverin…