This HTML5 document contains 34 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n17http://localhost/temp/predkladatel/
n13http://linked.opendata.cz/resource/domain/vavai/projekt/
n8http://linked.opendata.cz/ontology/domain/vavai/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/
rdfshttp://www.w3.org/2000/01/rdf-schema#
skoshttp://www.w3.org/2004/02/skos/core#
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n10http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F14%3A00225029%21RIV15-GA0-21230___/
n7http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n11http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n9http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00225029%21RIV15-GA0-21230___
rdf:type
skos:Concept n8:Vysledek
rdfs:seeAlso
http://aaai.org/ocs/index.php/WS/AAAIW14/paper/view/8820/8353
dcterms:description
Online search in games has always been a core interest of artificial intelligence. Advances made in search for perfect information games (such as Chess, Checkers, Go, and Backgammon) have led to AI capable of defeating the world's top human experts. Search in imperfect information games (such as Poker, Bridge, and Skat) is significantly more challenging due to the complexities introduced by hidden information. In this paper, we present Online Outcome Sampling (OOS), the first imperfect information search algorithm that is guaranteed to converge to an equilibrium strategy in two-player zero-sum games. We show that OOS avoids common problems encountered by existing search algorithms and we experimentally evaluate its convergence rate and practical performance against benchmark strategies in Liar's Dice and a variant of Goofspiel. We show that unlike with Information Set Monte Carlo Tree Search (ISMCTS) the exploitability of the strategies produced by OOS decreases as the amount of search time increases. In practice, OOS performs as well as ISMCTS in head-to-head play while producing strategies with lower exploitability given the same search time. Online search in games has always been a core interest of artificial intelligence. Advances made in search for perfect information games (such as Chess, Checkers, Go, and Backgammon) have led to AI capable of defeating the world's top human experts. Search in imperfect information games (such as Poker, Bridge, and Skat) is significantly more challenging due to the complexities introduced by hidden information. In this paper, we present Online Outcome Sampling (OOS), the first imperfect information search algorithm that is guaranteed to converge to an equilibrium strategy in two-player zero-sum games. We show that OOS avoids common problems encountered by existing search algorithms and we experimentally evaluate its convergence rate and practical performance against benchmark strategies in Liar's Dice and a variant of Goofspiel. We show that unlike with Information Set Monte Carlo Tree Search (ISMCTS) the exploitability of the strategies produced by OOS decreases as the amount of search time increases. In practice, OOS performs as well as ISMCTS in head-to-head play while producing strategies with lower exploitability given the same search time.
dcterms:title
Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization
skos:prefLabel
Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization
skos:notation
RIV/68407700:21230/14:00225029!RIV15-GA0-21230___
n5:aktivita
n6:P
n5:aktivity
P(GAP202/12/2054)
n5:dodaniDat
n9:2015
n5:domaciTvurceVysledku
Lisý, Viliam
n5:druhVysledku
n16:O
n5:duvernostUdaju
n11:S
n5:entitaPredkladatele
n10:predkladatel
n5:idSjednocenehoVysledku
44087
n5:idVysledku
RIV/68407700:21230/14:00225029
n5:jazykVysledku
n12:eng
n5:klicovaSlova
Monte Carlo Tree Search; imperfect information game; convergence
n5:klicoveSlovo
n7:Monte%20Carlo%20Tree%20Search n7:convergence n7:imperfect%20information%20game
n5:kontrolniKodProRIV
[4358D519C5CC]
n5:obor
n15:IN
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
3
n5:projekt
n13:GAP202%2F12%2F2054
n5:rokUplatneniVysledku
n9:2014
n5:tvurceVysledku
Lanctot, M. Bowling, M. Lisý, Viliam
n17:organizacniJednotka
21230