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This bibliography contains all known publications related to MCTS (414 at last count). Additional material such as presentations, web pages, repositories and so on may be found in the Resources page.
Please submit new entries to us with complete reference details and associated URL.
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[1]
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Bilal Kartal, John Koenig and Stephen J. Guy.
Generating Believable Stories in Large Domains.
In Ninth Artificial Intelligence and Interactive Digital Entertainment Conference, 2013.
[ bib |
.pdf ]
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[1]
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Amit Benbassat and Moshe Sipper.
Evomcts: Enhancing mcts-based players through genetic programming.
In Computational Intelligence in Games (CIG), 2013 IEEE
Conference on, pages 1-8. IEEE, 2013.
[ bib ]
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[2]
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Woodward H Folsom.
ScratchGo: An Integrated Approach to Playing Computer Go.
Technical report, 2013.
[ bib ]
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[3]
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Manuel Loth, Youssef Hamadi, and Marc Schoenauer.
Hybridizing Constraint Programming and Monte-Carlo Tree Search :
Application to the Job Shop problem.
In Learning and Intelligent OptimizatioN Conference, 2013.
[ bib ]
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[4]
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Diego Perez, Edward J. Powley, Daniel Whitehouse, Philipp Rohlfshagen, Spyridon
Samothrakis, Peter I. Cowling, and Simon M. Lucas.
Solving the Physical Travelling Salesman Problem: Tree Search and
Macro-Actions.
IEEE Trans. Comp. Intell. AI Games (accepted), 2013.
[ bib ]
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[5]
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J. A. M. Nijssen and Mark H. M. Winands.
Monte Carlo Tree Search for the Hide-and-Seek Game Scotland Yard.
IEEE Trans. Comp. Intell. AI Games, 4(4):282-294, December
2012.
[ bib |
DOI |
http ]
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[6]
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Daniel Beard, Philip Hingston, and Martin Masek.
Using Monte Carlo Tree Search for replanning in a multistage
simultaneous game.
In Proc. IEEE Congr. Evol. Comput., pages 1-8, Brisbane,
Australia, June 2012. Ieee.
[ bib |
DOI |
http ]
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[7]
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Lukasz Kaiser, Lukasz Stafiniak, and Faried Abu Zaid.
Toss Design and Specification.
Technical report, Aachen Univ., 2012.
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[8]
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Raphaël Féraud and Tanguy Furvoy.
A stochastic bandit algorithm for scratch games.
In Asian Conference on Machine Learning, pages 129-143, 2012.
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[9]
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Cameron Browne, Edward J. Powley, Daniel Whitehouse, Simon M. Lucas, Peter I.
Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon
Samothrakis, and Simon Colton.
A Survey of Monte Carlo Tree Search Methods.
IEEE Trans. Comp. Intell. AI Games, 4(1):1-43, 2012.
[ bib |
DOI |
http ]
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[10]
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Hendrik Baier and Mark H. M. Winands.
Beam Monte-Carlo Tree Search.
In Proc. IEEE Conf. Comput. Intell. Games, pages 227-233,
Granada, Spain, 2012.
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[11]
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Edward J. Powley, Daniel Whitehouse, and Peter I. Cowling.
Monte Carlo Tree Search with macro-actions and heuristic route
planning for the Physical Travelling Salesman Problem.
In Proc. IEEE Conf. Comput. Intell. Games, pages 234-241,
Granada, Spain, 2012.
[ bib ]
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[12]
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Erik Talvitie.
Learning Partially Observable Models Using Temporally Abstract
Decision Trees.
In Proc. Adv. Neur. Inform. Process. Sys., pages 827-835,
2012.
[ bib ]
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[13]
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Rémi Munos.
The optimistic principle applied to games, optimization and
planning: Towards Foundations of Monte-Carlo Tree Search.
Foundations and Trends in Machine Learning, xx(xx):1-110,
2012.
[ bib |
http ]
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[14]
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Mark Richards and Eyal Amir.
Information Set Generation in Partially Observable Games.
In Proc. Assoc. Adv. Artif. Intell., Toronto, Canada, 2012.
[ bib |
.pdf ]
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[15]
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Arturo Yee and Matías Alvarado.
Pattern Recognition and Monte-CarloTree Search for Go Gaming Better
Automation.
In Advances in Artificial Intelligence–IBERAMIA 2012, pages
11-20, Cartagena de Indias, Colombia, 2012.
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[16]
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Olivier Buffet, CS Lee, Woan-tyng Lin, and Olivier Teytaud.
Optimistic Heuristics for MineSweeper.
In International Computer Symposium, Hualien, Taiwan, 2012.
[ bib |
http ]
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[17]
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Mandy J. W. Tak, Mark H. M. Winands, and Yngvi Björnsson.
N-Grams and the Last-Good-Reply Policy Applied in General Game
Playing.
IEEE Trans. Comp. Intell. AI Games, 4(2):73-83, 2012.
[ bib ]
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[18]
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Peter I. Cowling, Edward J. Powley, and Daniel Whitehouse.
Information Set Monte Carlo Tree Search.
IEEE Trans. Comp. Intell. AI Games, 4(2):120-143, 2012.
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[19]
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Weijia Wang and Michèle Sebag.
Multi-objective Monte-Carlo Tree Search.
In Asian Conference on Machine Learning, pages 507-522, 2012.
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[20]
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Munir Naveed, Diane Kitchin, Andrew Crampton, Lukáš Chrpa, and Peter
Gregory.
A Monte-Carlo Path Planner for Dynamic and Partially Observable
Environments.
In Proc. IEEE Conf. Comput. Intell. Games, pages 211-218,
Granada, Spain, 2012.
[ bib ]
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[21]
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Cheng-wei Chou, Ping-chiang Chou, Chang-shing Lee, David Lupien Saint-pierre,
Olivier Teytaud, Mei-hui Wang, Li-wen Wu, and Shi-jim Yen.
Strategic Choices : Small Budgets and Simple Regret.
In TAAI, pages 1-14, 2012.
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[22]
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Ralf Funke.
From Gobble to Zen The Quest for Truly Intelligent Software and the
Monte Carlo Revolution in Go.
In Beyond AI: Artificial Dreams, pages 10-20, Pilsen, Czech
Republic, 2012.
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[23]
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Peter I. Cowling, Colin D. Ward, and Edward J. Powley.
Ensemble Determinization in Monte Carlo Tree Search for the
Imperfect Information Card Game Magic: The Gathering.
IEEE Trans. Comp. Intell. AI Games, 4(4):241-257, 2012.
[ bib |
DOI ]
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[24]
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Tom Pepels and Mark H. M. Winands.
Enhancements for Monte-Carlo Tree Search in Ms Pac-Man.
In Proc. IEEE Conf. Comput. Intell. Games, pages 265-272,
Granada, Spain, 2012.
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[25]
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Diego Perez, Philipp Rohlfshagen, and Simon M Lucas.
Monte Carlo Tree Search: Long-term versus Short-term Planning.
In Proc. IEEE Conf. Comput. Intell. Games, pages 219-226,
Granada, Spain, 2012.
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[26]
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Pierre Perick, David L. St-Pierre, Francis Maes, and Damien Ernst.
Comparison of Different Selection Strategies in Monte-Carlo Tree
Search for the Game of Tron.
In Proc. IEEE Conf. Comput. Intell. Games, pages 242-249,
Granada, Spain, 2012.
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[27]
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Hao-yun Liu, I-chen Wu, Hao-hua Kang, and Ting-fu Liao.
System Demonstration for Generic Game Development Framework.
In 2012 Conference on Technologies and Applications of
Artificial Intelligence, pages 323-326, 2012.
[ bib |
DOI ]
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[28]
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Huy Nguyen, Kokolo Ikeda, and Bac Le.
Extracting important patterns for building state-action evaluation
function in Othello.
In IEEE Conf. Technologies and Applications of Artificial
Intelligence (TAAI), pages 278-283, 2012.
[ bib |
DOI ]
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[29]
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Arisoa S Randrianasolo and Larry D Pyeatt.
Using Local Regression in Monte Carlo Tree Search.
In Proc 11th IEEE International Conference on Machine Learning
and Applications (ICMLA), number 2, pages 500-503, 2012.
[ bib |
DOI ]
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[30]
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Ngo Anh Vien and Wolfgang Ertel.
Monte Carlo Tree Search for Bayesian Reinforcement Learning.
In Proc 11th IEEE International Conference on Machine Learning
and Applications (ICMLA), pages 138-144, 2012.
[ bib |
DOI ]
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[31]
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Jean-Baptiste Hoock and Jacques Bibai.
Solving a goal-planning task in the mash project.
In Technologies and Applications of Artificial Intelligence
(TAAI), 2012 Conference on, pages 199-204. IEEE, 2012.
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[32]
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Kirk L. Kroeker.
A New Benchmark for Artificial Intelligence.
Commun. ACM, 54(8):13-15, August 2011.
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DOI |
http ]
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[33]
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Sylvain Gelly and David Silver.
Monte-Carlo tree search and rapid action value estimation in
computer Go.
Artif. Intell., 175(11):1856-1875, July 2011.
[ bib |
DOI |
http ]
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[34]
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Jonathan Rubin and Ian Watson.
Computer poker: A review.
Artif. Intell., 175(5-6):958-987, April 2011.
[ bib |
DOI |
http ]
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[35]
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Hendrik Baier and Mark H. M. Winands.
Time Management for Monte-Carlo Tree Search in Go.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
39-51, Tilburg, The Netherlands, 2011. Springer.
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[36]
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Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe, and Kokolo Ikeda.
Accelerated UCT and Its Application to Two-Player Games.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
1-12, Tilburg, The Netherlands, 2011. Springer.
[ bib ]
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[37]
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Baba Satomi, Yongjoon Joe, Atsushi Iwasaki, and Makoto Yokoo.
Real-Time Solving of Quantified CSPs Based on Monte-Carlo Game Tree
Search.
In Proc. 22nd Int. Joint Conf. Artif. Intell., pages 655-662,
Barcelona, Spain, 2011.
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[38]
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Olivier Teytaud and Sébastien Flory.
Upper Confidence Trees with Short Term Partial Information.
In Cecilia Di Chio, Stefano Cagnoni, Carlos Cotta, Marc Ebner,
Anikó Ekárt, Anna Esparcia-Alcázar, Juan Julián Merelo
Guervós, Ferrante Neri, Mike Preuss, Hendrik Richter, Julian Togelius,
and Georgios N. Yannakakis, editors, Proc. Applicat. Evol. Comput. 1,
LNCS 6624, pages 153-162, Torino, Italy, 2011.
[ bib ]
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[39]
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Jean Méhat and Tristan Cazenave.
Tree Parallelization of Ary on a Cluster.
In Proc. Int. Joint Conf. Artif. Intell., pages 39-43,
Barcelona, Spain, 2011.
[ bib ]
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[40]
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Leandro Soriano Marcolino.
Multi-Agent Monte Carlo Go.
M.sc., Future Univ. Hakodate, 2011.
[ bib ]
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[41]
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Jan A. Stankiewicz, Mark H. M. Winands, and Jos W. H. M. Uiterwijk.
Monte-Carlo Tree Search Enhancements for Havannah.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
60-71, Tilburg, The Netherlands, 2011.
[ bib ]
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[42]
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Bernard Helmstetter, Chang-Shing Lee, Fabien Teytaud, Olivier Teytaud, Mei-Hui
Wang, and Shi-Jim Yen.
Random positions in Go.
In Proc. IEEE Conf. Comput. Intell. Games, pages 250-257,
Seoul, South Korea, 2011.
[ bib ]
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[43]
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Fan Xie, Hootan Nakhost, and Martin Müller.
A Local Monte Carlo Tree Search Approach in Deterministic Planning.
In Proc. Assoc. Adv. Artif. Intell., pages 1832-1833, San
Francisco, California, 2011.
[ bib ]
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[44]
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Kien Quang Nguyen, Takeru Miyama, Asami Yamada, Takashi Ashida, and Ruck
Thawonmas.
ICE gUCT.
Technical report, Int. Comput. Entertain. Lab., Ritsumeikan Univ.,
2011.
[ bib ]
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[45]
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Tobias Mahlmann, Julian Togelius, and Georgios N. Yannakakis.
Modelling and evaluation of complex scenarios with the Strategy Game
Description Language.
In Proc. IEEE Conf. Comput. Intell. Games, pages 174-181,
Seoul, South Korea, 2011.
[ bib ]
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[46]
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Shih-Chieh Huang.
New Heuristics for Monte Carlo Tree Search Applied to the Game
of Go.
Ph.d. dissertation, Nat. Taiwan Normal Univ., Taipei, 2011.
[ bib ]
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[47]
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Bruce Kwong-Bun Tong and Chi Wan Sung.
A Monte-Carlo Approach for Ghost Avoidance in the Ms. Pac-Man Game.
In Proc. IEEE Consum. Elect. Soc. Games Innov. Conf., pages
1-8, Hong Kong, 2011.
[ bib |
http ]
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[48]
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Petr Baudiš and Jean-loup Gailly.
PACHI: State of the Art Open Source Go Program.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
24-38, Tilburg, The Netherlands, 2011. Springer.
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[49]
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Bruno Bouzy, Marc Métivier, and Damien Pellier.
MCTS Experiments on the Voronoi Game.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
96-107, Tilburg, The Netherlands, 2011. Springer.
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[50]
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Adrien Couëtoux, Jean-Baptiste Hoock, Nataliya Sokolovska, Olivier Teytaud,
and Nicolas Bonnard.
Continuous Upper Confidence Trees.
In Proc. Learn. Intell. Optim., pages 433-445, Rome, Italy,
2011.
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[51]
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Mark H. M. Winands and Yngvi Björnsson.
αβ-based Play-outs in Monte-Carlo Tree Search.
In IEEE Conf. Comput. Intell. Games, pages 110-117, Seoul,
South Korea, 2011.
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[52]
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Hilmar Finnsson and Yngvi Björnsson.
CadiaPlayer: Search-Control Techniques.
Künstliche Intelligenz, 25(1):9-16, January 2011.
[ bib |
DOI |
http ]
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[53]
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Leandro Soriano Marcolino and Hitoshi Matsubara.
Multi-Agent Monte Carlo Go.
In Turner, Yolum, Sonenberg, and Stone, editors, Proc. 10th Int.
Conf. Auton. Agents Multi. Sys., pages 21-28, Taipei, Taiwan, 2011.
[ bib |
.pdf ]
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[54]
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Nozomu Ikehata and Takeshi Ito.
Monte-Carlo Tree Search in Ms. Pac-Man.
In Proc. IEEE Conf. Comput. Intell. Games, pages 39-46, Seoul,
South Korea, 2011.
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[55]
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Jean Méhat and Tristan Cazenave.
A Parallel General Game Player.
Künstliche Intelligenz, 25(1):43-47, 2011.
[ bib |
.pdf ]
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[56]
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Daniel Whitehouse, Edward J. Powley, and Peter I. Cowling.
Determinization and Information Set Monte Carlo Tree Search for the
Card Game Dou Di Zhu.
In Proc. IEEE Conf. Comput. Intell. Games, pages 87-94, Seoul,
South Korea, 2011.
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[57]
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Alan Fern and Paul Lewis.
Ensemble Monte-Carlo Planning: An Empirical Study.
In Proc. 21st Int. Conf. Automat. Plan. Sched., pages 58-65,
Freiburg, Germany, 2011.
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[58]
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Raghuram Ramanujan, Ashish Sabharwal, and Bart Selman.
On the Behavior of UCT in Synthetic Search Spaces.
In Proc. 21st Int. Conf. Automat. Plan. Sched., Freiburg,
Germany, 2011.
[ bib |
.pdf ]
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[59]
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Ping-Chiang Chou, Hassen Doghmen, Chang-Shing Lee, Fabien Teytaud, Olivier
Teytaud, Hui-Ming Wang, Mei-Hui Wang, Li-Wen Wu, and Shi-Jim Yen.
Computational and Human Intelligence in Blind Go.
In Proc. IEEE Conf. Comput. Intell. Games, pages 235-242,
Seoul, South Korea, 2011.
[ bib ]
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[60]
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Ashish Sabharwal and Horst Samulowitz.
Guiding Combinatorial Optimization with UCT.
In Proc. 21st Int. Conf. Automat. Plan. Sched., Freiburg,
Germany, 2011.
[ bib |
.pdf ]
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[61]
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Jiao Wang, Shiyuan Li, Jitong Chen, Xin Wei, Huizhan Lv, and Xinhe Xu.
4*4-Pattern and Bayesian Learning in Monte-Carlo Go.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
108-120, Tilburg, The Netherlands, 2011. Springer.
[ bib ]
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[62]
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Tobias Mahlmann, Julian Togelius, and Georgios N. Yannakakis.
Towards Procedural Strategy Game Generation: Evolving Complementary
Unit Types.
In Proc. Applicat. Evol. Comput., LNCS 6624, pages 93-102,
Torino, Italy, 2011.
[ bib ]
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[63]
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Arpad Rimmel, Fabien Teytaud, and Tristan Cazenave.
Optimization of the Nested Monte-Carlo Algorithm on the Traveling
Salesman Problem with Time Windows.
In Proc. Applicat. Evol. Comput. 2, LNCS 6625, pages 501-510,
Torino, Italy, 2011.
[ bib ]
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[64]
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Sébastien Bubeck, Rémi Munos, Gilles Stoltz, and Csaba Szepesvári.
X-Armed Bandits.
J. Mach. Learn. Res., 12:1587-1627, 2011.
[ bib |
http ]
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[65]
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Edward J. Powley, Daniel Whitehouse, and Peter I. Cowling.
Determinization in Monte-Carlo Tree Search for the card game Dou Di
Zhu.
In Proc. Artif. Intell. Simul. Behav., pages 17-24, York,
United Kingdom, 2011.
[ bib ]
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[66]
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David L. St-Pierre, Quentin Louveaux, and Olivier Teytaud.
Online Sparse Bandit for Card Games.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
295-305, Tilburg, The Netherlands, 2011. Springer.
[ bib ]
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[67]
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Christopher D. Rosin.
Nested Rollout Policy Adaptation for Monte Carlo Tree Search.
In Proc. 22nd Int. Joint Conf. Artif. Intell., pages 649-654,
Barcelona, Spain, 2011.
[ bib |
.pdf ]
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[68]
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Zahy Bnaya, Ariel Felner, Solomon Eyal Shimony, Dror Fried, and Olga Maksin.
Repeated-task Canadian traveler problem.
In Proc. Symp. Combin. Search, pages 24-30, Barcelona, Spain,
2011.
[ bib ]
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[69]
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Marc Ponsen, Steven de Jong, and Marc Lanctot.
Computing Approximate Nash Equilibria and Robust Best-Responses
Using Sampling.
J. Artif. Intell. Res., 42:575-605, 2011.
[ bib ]
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[70]
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Lars Schaefers, Marco Platzner, and Ulf Lorenz.
UCT-Treesplit - Parallel MCTS on Distributed Memory.
In Proc. 21st Int. Conf. Automat. Plan. Sched., Freiburg,
Germany, 2011.
[ bib ]
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[71]
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Fan Xie, Hootan Nakhost, and Martin Müller.
A Local Monte Carlo Tree Search Approach in Deterministic Planning
Monte Carlo (submission).
In Proc. Assoc. Adv. Artif. Intell., San Francisco, California,
2011.
[ bib ]
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[72]
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Alessandro Previti, Raghuram Ramanujan, Marco Schaerf, and Bart Selman.
Monte-Carlo Style UCT Search for Boolean Satisfiability.
In Proc. 12th Int. Conf. Ital. Assoc. Artif. Intell., LNCS
6934, pages 177-188, Palermo, Italy, 2011.
[ bib ]
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[73]
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Spyridon Samothrakis, Wivenhoe Park, and Simon M. Lucas.
Approximating n-player Behavioural Strategy Nash Equilibria Using
Coevolution.
In Proc. Gen. Evol. Comput. Conf., pages 1107-1114, Dublin,
Ireland, 2011.
[ bib ]
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[74]
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David Robles, Philipp Rohlfshagen, and Simon M. Lucas.
Learning Non-Random Moves for Playing Othello: Improving Monte Carlo
Tree Search.
In Proc. IEEE Conf. Comput. Intell. Games, pages 305-312,
Seoul, South Korea, 2011.
[ bib ]
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[75]
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J. A. M. Nijssen and Mark H. M. Winands.
Monte-Carlo Tree Search for the Game of Scotland Yard.
In Proc. IEEE Conf. Comput. Intell. Games, pages 158-165,
Seoul, South Korea, 2011.
[ bib |
http ]
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[76]
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Sébastien Bubeck, Rémi Munos, and Gilles Stoltz.
Pure Exploration in Finitely-Armed and Continuously-Armed Bandits.
Theor. Comput. Sci., 412:1832-1852, 2011.
[ bib |
.pdf ]
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[77]
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Tristan Cazenave.
Monte-Carlo Approximation of Temperature.
Games of No Chance, 4, 2011.
[ bib |
.pdf ]
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[78]
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Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe, and Kokolo Ikeda.
Accelerated UCT and Its Application to Two-Player Games.
In Adv. Comput. Games 13, Tilburg, The Netherlands, 2011.
[ bib ]
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[79]
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Richard J. Lorentz.
An MCTS Program to Play EinStein Würfelt Nicht!
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
52-59, Tilburg, The Netherlands, 2011. Springer.
[ bib ]
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[80]
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David Auger.
Multiple Tree for Partially Observable Monte-Carlo Tree Search.
In Proc. Evol. Games., pages 53-62, Torino, Italy, 2011.
[ bib |
arXiv |
.pdf ]
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[81]
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Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, and David Silver.
A Monte-Carlo AIXI Approximation.
J. Artif. Intell. Res., 40:95-142, 2011.
[ bib |
arXiv ]
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[82]
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Bruce Kwong-Bun Tong, Chun Man Ma, and Chi Wan Sung.
A Monte-Carlo Approach for the Endgame of Ms. Pac-Man.
In Proc. IEEE Conf. Comput. Intell. Games, pages 9-15, Seoul,
South Korea, 2011.
[ bib ]
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[83]
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Raghuram Ramanujan and Bart Selman.
Trade-Offs in Sampling-Based Adversarial Planning.
In Proc. 21st Int. Conf. Automat. Plan. Sched., pages 202-209,
Freiburg, Germany, 2011.
[ bib ]
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[84]
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S. R. K. Branavan, David Silver, and Regina Barzilay.
Non-Linear Monte-Carlo Search in Civilization II.
In Proc. 22nd Int. Joint Conf. Artif. Intell., pages
2404-2410, Barcelona, Spain, 2011.
[ bib ]
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[85]
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N. G. P. Den Teuling.
Monte-Carlo Tree Search for the Simultaneous Move Game Tron.
B.sc. thesis, Univ. Maastricht, Netherlands, 2011.
[ bib |
.pdf ]
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[86]
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Spyridon Samothrakis, David Robles, and Simon M. Lucas.
Fast Approximate Max-n Monte-Carlo Tree Search for Ms Pac-Man.
IEEE Trans. Comp. Intell. AI Games, 3(2):142-154, 2011.
[ bib ]
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[87]
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Jan A. Stankiewicz.
Knowledge-Based Monte-Carlo Tree Search in Havannah.
M.s. thesis, Maastricht Univ., Netherlands, 2011.
[ bib ]
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[88]
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Chris Mansley, Ari Weinstein, and Michael L. Littman.
Sample-Based Planning for Continuous Action Markov Decision
Processes.
In Proc. 21st Int. Conf. Automat. Plan. Sched., pages 335-338,
Freiburg, Germany, 2011.
[ bib |
.pdf ]
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[89]
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John Asmuth and Michael L. Littman.
Learning is planning: near Bayes-optimal reinforcement learning via
Monte-Carlo tree search.
In Proc. Conf. Uncert. Artif. Intell., pages 19-26, Barcelona,
Spain, 2011.
[ bib ]
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[90]
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J. A. M. Nijssen and Mark H. M. Winands.
Playout Search for Monte-Carlo Tree Search in Multi-player Games.
In Proc. 13th Int. Conf. Adv. Comput. Games, LNCS 7168, pages
72-83, Tilburg, The Netherlands, 2011. Springer.
[ bib ]
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[91]
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Cameron Browne.
Towards MCTS for Creative Domains.
In Proc. Int. Conf. Comput. Creat., pages 96-101, Mexico City,
Mexico, 2011.
[ bib ]
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[92]
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John Asmuth and Michael L. Littman.
Approaching Bayes-optimalilty using Monte-Carlo tree search.
In Proc. 21st Int. Conf. Automat. Plan. Sched., Freiburg,
Germany, 2011.
[ bib |
.pdf ]
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[93]
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Maarten Peter Dirk Schadd.
Selective Search in Games of Different Complexity.
Ph.d. dissertation, Maastricht Univ., Netherlands, 2011.
[ bib ]
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[94]
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David Tolpin and Solomon Eyal Shimony.
Doing Better Than UCT: Rational Monte Carlo Sampling in Trees.
Technical report, Ben-Gurion University, Israel, 2011.
[ bib |
arXiv ]
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[95]
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Hilmar Finnsson and Yngvi Björnsson.
Game-Tree Properties and MCTS Performance.
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Displays a single web form on the page that matches the web form ID. Please note that you need to customize the form before you insert it onto the page as a module. One form cannot be inserted as a module and it is the form in eCommerce in the registration layout. The formID parameter should not be changed and it is assigned by module manager.
To insert a webform as a module make sure you have this checkbox checked:
Inserting a webform as a module instead of the "classic" HTML code is most useful when you have one webform inserted in multiple pages. If you need to make a change you can edit the form's HTML code in a single panel (THe "Customize Web Form" panel - see the screenshot below) as opposed to editing all the pages the form is inserted to.{module_webform,formID}
{module_webform,271293}
- outputs the HTML code for the webform with the ID 271293


The shadow of things to come
Lotus Notes and Domino 8.5 are closing in on their release date. There is a lot of buzz around XPages. But IBM doesn't stop here. Development for 8.5.1 is in full swing and planning for 9.0 has kicked off long ago. Of course (can you spell NDA) I can't tell what's happening next in the Domino space. But I can share a screenshot from my workstation (proudly running Ubuntu):
Nota bene: I do not work in development, I don't make statements what they do or will do.
Nota bene: I do not work in development, I don't make statements what they do or will do.
Posted by Stephan H Wissel on 02 November 2008 | Comments (7) | categories: IBM Notes Lotus Notes


Practical Exploitation of the VPN 'PortFail' IP Leak against Torrent Users.
26 Nov 2015Today there was a critical vulnerability in various VPN providers disclosed by the Perfect Privacy VPN’s security team. This vulnerability can lead to “unmasking” or “decloaking” of VPN users under certain conditions, and after some analysis of how it works, I determined that this issue is of utmost importance to those who use VPN services to mask their bittorrent traffic from MAFIAA scum, as it can allow for ‘uncloaking’ of Bittorrent users quite readily.
In the spirit of openness, and now that the bugs details are public, I have decided to outline an example attack which would allow for someone to decloak Torrent users hiding behind vulnerable VPN’s. While I am not providing some automated deanonymizing toolkit for getting the job done, the instructions below should suffice for someone else to reproduce the attack and demonstrate its effectiveness in deanonymizing users.
How to be evil:
- Sell your soul to the devil and become the kind of wanker who wants to help sue bittorrent users.
- Bulk register accounts on VPN’s that have been found to be vulnerable to this attack.
- For torrents of interest, become a peer in the peer swarm, to gather IP’s that are “protected” by VPN’s.
- Match IP’s of peers in the swarm against a list of known-vulnerable VPN exit IP’s.
- Spin up a VPN connection using the same exit server as the client(s) you wish to “uncloak”.
- Port forward torrent port using the VPN and then join the peer swarm. You want to look like a good peer. Perhaps even deny non-interesting peers access, or whatever method you need to do to be attractive to your victim(s) of interest.
- When the peers of interest connect to you, they will reveal their real IP address.

As you can see, this attack is well within the capabilities of almost anyone - no nation state capabilities needed. You would simply need to have the budget to buy accounts on multiple VPN services (to identify vulnerable ones, and also for the deanonymizing step), a list of torrents that were of interest to you (i.e. ones that you are ‘protecting’ the copyright of, or some such bullshit.
I believe this kind of attack is probably going to be used heavily by copyright-litigation firms trying to prosecute Torrent users in the future, so it is probably best to double check that the VPN provider you are using does not suffer this vulnerability. If they do, notify them, and make sure they fix it.