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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00223904%21RIV15-GA0-21230___
rdf:type
n9:Vysledek skos:Concept
rdfs:seeAlso
http://link.springer.com/chapter/10.1007/978-3-319-05428-5_3
dcterms:description
Monte Carlo Tree Search (MCTS) has become a widely popular sampled-based search algorithm for two-player games with perfect information. When actions are chosen simultaneously, players may need to mix between their strategies. In this paper, we discuss the adaptation of MCTS to simultaneous move games. We introduce a new algorithm, Online Outcome Sampling (OOS), that approaches a Nash equilibrium strategy over time. We compare both head-to-head performance and exploitability of several MCTS variants in Goofspiel. We show that regret matching and OOS perform best and that all variants produce less exploitable strategies than UCT. Monte Carlo Tree Search (MCTS) has become a widely popular sampled-based search algorithm for two-player games with perfect information. When actions are chosen simultaneously, players may need to mix between their strategies. In this paper, we discuss the adaptation of MCTS to simultaneous move games. We introduce a new algorithm, Online Outcome Sampling (OOS), that approaches a Nash equilibrium strategy over time. We compare both head-to-head performance and exploitability of several MCTS variants in Goofspiel. We show that regret matching and OOS perform best and that all variants produce less exploitable strategies than UCT.
dcterms:title
Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel
skos:prefLabel
Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel Monte Carlo Tree Search in Simultaneous Move Games with Applications to Goofspiel
skos:notation
RIV/68407700:21230/14:00223904!RIV15-GA0-21230___
n5:aktivita
n10:P
n5:aktivity
P(GAP202/12/2054)
n5:dodaniDat
n18:2015
n5:domaciTvurceVysledku
Lisý, Viliam
n5:druhVysledku
n16:D
n5:duvernostUdaju
n20:S
n5:entitaPredkladatele
n14:predkladatel
n5:idSjednocenehoVysledku
30497
n5:idVysledku
RIV/68407700:21230/14:00223904
n5:jazykVysledku
n17:eng
n5:klicovaSlova
Monte-Carlo tree search; simulaneous-move game; game playing; convergence
n5:klicoveSlovo
n7:simulaneous-move%20game n7:Monte-Carlo%20tree%20search n7:game%20playing n7:convergence
n5:kontrolniKodProRIV
[2B5DB75A6099]
n5:mistoKonaniAkce
Beijing
n5:mistoVydani
Cham
n5:nazevZdroje
Computer Games
n5:obor
n13:IN
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
3
n5:projekt
n15:GAP202%2F12%2F2054
n5:rokUplatneniVysledku
n18:2014
n5:tvurceVysledku
Lanctot, M. Winands, M. H. M. Lisý, Viliam
n5:typAkce
n22:WRD
n5:zahajeniAkce
2013-08-03+02:00
s:issn
1865-0929
s:numberOfPages
16
n4:doi
10.1007/978-3-319-05428-5_3
n12:hasPublisher
Springer International Publishing AG
n21:isbn
978-3-319-05427-8
n8:organizacniJednotka
21230