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Learning opening books in partially observable games: using random seeds in Phantom Go

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Date
2016
Link to item file
https://hal.inria.fr/hal-01413229
Dewey
Intelligence artificielle
Sujet
Phantom Go
Conference name
IEEE CIG 2016 - Computer intelligence and Games
Conference date
09-2016
Conference city
Santorini
Conference country
Greece
URI
https://basepub.dauphine.fr/handle/123456789/20846
Collections
  • LAMSADE : Publications
Metadata
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Author
Cazenave, Tristan
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Liu, Jialin
Teytaud, Fabien
Teytaud, Olivier
Type
Communication / Conférence
Abstract (EN)
Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is far from being negligible. Then, we apply two different existing algorithms for selecting good seeds and good probability distributions over seeds. This mainly leads to learning an opening book. We apply this to Phantom Go, which, as all phantom games, is hard for opening book learning. We improve the winning rate from 50% to 70% in 5x5 against the same AI, and from approximately 0% to 40% in 5x5, 7x7 and 9x9 against a stronger (learning) opponent.

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