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Volumn , Issue , 2007, Pages 35-39

Move ordering vs heavy playouts: Where should heuristics be applied in Monte Carlo Go?

Author keywords

Artificial intelligence; Go game; Heuristics; Monte Carlo; UCT

Indexed keywords

ARTIFICIAL INTELLIGENCE;

EID: 84898662776     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (13)

References (14)
  • 1
    • 62949190602 scopus 로고
    • New York, NY: American Go Association
    • Baker, K. The Way to Go. 1986. New York, NY: American Go Association.
    • (1986) The Way to Go
    • Baker, K.1
  • 2
    • 0035479281 scopus 로고    scopus 로고
    • Computer go: An ai oriented survey
    • Bouzy, B., and Cazenave, T. 2001. Computer Go: an AI Oriented Survey. Artificial Intelligence 132(1):39-103.
    • (2001) Artificial Intelligence , vol.132 , Issue.1 , pp. 39-103
    • Bouzy, B.1    Cazenave, T.2
  • 7
    • 80053628578 scopus 로고    scopus 로고
    • Monte carlo planning in rts games
    • Colchester
    • Chung, M., Buro, M., and Schaeffer, J. 2005. Monte Carlo Planning in RTS Games, CIG 2005, Colchester
    • (2005) CIG 2005
    • Chung, M.1    Buro, M.2    Schaeffer, J.3
  • 9
    • 38849139064 scopus 로고    scopus 로고
    • Computing elo ratings of move patterns in the game of go
    • Draft, submitted to
    • Coulom, R. 2007. Computing Elo ratings of move patterns in the game of Go. Draft, submitted to ICG A Computer Games Workshop 2007.
    • (2007) ICG A Computer Games Workshop 2007
    • Coulom, R.1
  • 12


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.