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Integrating Factorization Ranked Features in MCTS: An Experimental Study

  • Chenjun XiaoEmail author
  • Martin Müller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 705)

Abstract

Recently, Factorization Bradley-Terry (FBT) model is introduced for fast move prediction in the game of Go. It has been shown that FBT outperforms the state-of-the-art fast move prediction system of Latent Factor Ranking (LFR). In this paper, we investigate the problem of integrating feature knowledge learned by FBT model in Monte Carlo Tree Search. We use the open source Go program Fuego as the test platform. Experimental results show that the FBT knowledge is useful in improving the performance of Fuego.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computing ScienceUniversity of AlbertaEdmontonCanada

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