Fuego-Related Publications

Publications Describing (Aspects of) Fuego

• M. Enzenberger, M. Müller, B. Arneson and R. Segal. Fuego - An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search IEEE Transactions on Computational Intelligence and AI in Games, 2(4), 259-270. Special issue on Monte Carlo Techniques and Computer Go, 2010.
  • DOI link (paper as printed in TCIAIG, access via IEEE)
  • final author version (free download)
• R. Segal. On the scalability of parallel UCT. Proceedings of the 7th international conference on Computers and games, CG 2010, pages 36-47,Springer LNCS 6515, 2011.
  • DOI link (access via SpringerLink)
• M. Müller. Fuego-GB Prototype at the Human machine competition in Barcelona 2010: a Tournament Report and Analysis. Technical Report TR 10-08, Dept. of Computing Science, University of Alberta, Edmonton, Alberta, Canada, 2010.
• M. Enzenberger and M. Müller. A lock-free multithreaded Monte-Carlo tree search algorithm. Advances in Computer Games 12, Pamplona, Spain, 2009.
• M. Müller. Fuego at the Computer Olympiad in Pamplona 2009 : a tournament report. Technical Report TR 09-09, Dept. of Computing Science. University of Alberta, Edmonton, Alberta, Canada, 2009.

Publications using Fuego in their Computer Go Research

• J. Hashimoto, A. Kishimoto, K. Yoshizoe, and K. Ikeda. Accelerated UCT and Its Application to Two-Player Games. To appear in Advances in Computer Games 13.
• Papers about pachi often use Fuego for comparisons and testing.
• Takeuchi, S.; Kaneko, T.; Yamaguchi, K. Evaluation of Game Tree Search Methods by Game Records. IEEE Transactions on Computational Intelligence and AI in Games, 2 (4), 288 - 302, Dec. 2010.
• Yusuke Soejima, Akihiro Kishimoto and Osamu Watanabe. Evaluating Root Parallelization in Go, IEEE Transactions on Computational Intelligence and AI in Games, Volume 2, Number 4, pages 278-287, 2010
• D. Silver's RLGO. See:
  • D. Silver, R. Sutton and M. Müller. Temporal-Difference Search in Computer Go. Machine Learning 87(2), 183-219, 2012.
  • D. Silver. Reinforcement Learning and Simulation-Based Search in Computer Go. PhD thesis, University of Alberta, 2009.
• L. S. Marcolino's Multi-Agent Monte Carlo Go. See:
  • L. S. Marcolino, "Multi-Agent Monte Carlo Go", Master's Thesis, advised by H. Matsubara. School of Systems Information Science at Future University Hakodate, Japan, August 2011. website with download links
  • L. S. Marcolino, H. Matsubara, "Multi-Agent Monte Carlo Go", Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems, May 2011. website with download links
• S. Takeuchi, T. Kaneko, and K. Yamaguchi. Evaluation of Monte Carlo Tree Search and the Application to Go. Computational Intelligence in Games (CIG 08), 191-198, 2008.

Publications using Fuego in other Computer Games Research

• The MoHex Hex playing program uses the Monte Carlo search engine and various other components of Fuego. See R. Hayward's publications page.
• D. Tom's studies of UCT and RAVE in an artificial game use the Fuego framework.
  • D. Tom and M. Müller. Computational Experiments with the RAVE Heuristic. LNCS 6515, 69-80, Springer 2011. DOI link
  • D. Tom. Investigating UCT and RAVE: steps towards a more robust method. MSc thesis, University of Alberta, 2010.
  • D. Tom and M. Müller. A study of UCT and its enhancements, 2009. Advances in Computer Games 12, LNCS 6048, pages 55-64, Springer. DOI link