Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing
Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transpositio...
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| Vydané v: | IEEE transactions on computational intelligence and AI in games. Ročník 2; číslo 4; s. 271 - 277 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.12.2010
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| ISSN: | 1943-068X, 1943-0698 |
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| Abstract | Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transposition tables (UCT+T), and a simple combination of NMC and UCT+T (MAX) on single-player games of the past General Game Playing (GGP) competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version. |
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| AbstractList | Monte Carlo tree search (MCTS) has been recently very successful for game playing, particularly for games where the evaluation of a state is difficult to compute, such as Go or General Games. We compare nested Monte Carlo (NMC) search, upper confidence bounds for trees (UCT-T), UCT with transposition tables (UCT+T), and a simple combination of NMC and UCT+T (MAX) on single-player games of the past General Game Playing (GGP) competitions. We show that transposition tables improve UCT and that MAX is the best of these four algorithms. Using UCT+T, the program Ary won the 2009 GGP competition. MAX and NMC are slight improvements over this 2009 version. |
| Author | Cazenave, Tristan Méhat, Jean |
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| Cites_doi | 10.3166/ria.23.203-220 10.1109/TCIAIG.2009.2018703 10.3166/ria.23.183-202 10.1109/TCIAIG.2009.2018702 10.1007/978-3-642-02846-5_28 10.1109/CIG.2008.5035667 10.1145/1273496.1273531 10.1613/jair.2490 |
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| References | ref13 coulom (ref10) 2006; 4630 gomes (ref15) 1997 ref17 love (ref19) 2006 cazenave (ref4) 2007 finnsson (ref12) 2008 ref8 (ref1) 2010 thielscher (ref22) 2009 leyton-brown (ref18) 2003; 18 ref7 kocsis (ref16) 2006; 4212 schiffel (ref21) 2007 ref3 ref6 cazenave (ref5) 2009 coulom (ref11) 2007; 30 berlekamp (ref2) 1982 gelly (ref14) 2008 clune (ref9) 2007 xu (ref23) 2008; 32 schadd (ref20) 2008 |
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| SubjectTerms | Algorithm design and analysis Artificial intelligence Classification algorithms Competition Computer simulation Decision trees Games General Game Playing (GPP) Monte Carlo methods nested Monte-Carlo search Searching single-player games Tables Trees upper confidence bounds for trees (UCT) |
| Title | Combining UCT and Nested Monte Carlo Search for Single-Player General Game Playing |
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