activity
20122026
most citedA Survey of Deep Meta-Learning

374 citations · 827 across the 88 of their papers we have counts for

collaborators
Showing 2014 · cs.AIShow all

6 papers · 2 filters

cs.AI2014★ 4 cited

Why Local Search Excels in Expression Simplification

Ben Ruijl, Aske Plaat, Jos Vermaseren +1

Simplifying expressions is important to make numerical integration of large expressions from High Energy Physics tractable. To this end, Horner's method can be used. Finding suitab…

cs.AI2014★ 3 cited

HEPGAME and the Simplification of Expressions

Ben Ruijl, Jos Vermaseren, Aske Plaat +1

Advances in high energy physics have created the need to increase computational capacity. Project HEPGAME was composed to address this challenge. One of the issues is that numerica…

cs.AI2014★ 5 cited

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…

cs.AI2014★ 3 cited

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…

cs.AI2014★ 2 cited

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…

cs.AI2014

MTD(f), A Minimax Algorithm Faster Than NegaScout

Aske Plaat

MTD(f) is a new minimax search algorithm, simpler and more efficient than previous algorithms. In tests with a number of tournament game playing programs for chess, checkers and Ot…