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  1. IEEE Transactions on Computational Intelligence and AI in Games
  2. Year : 2015 Volume : 7
  3. Issue 4
  4. Distributed Monte Carlo Tree Search: A Novel Technique and its Application to Computer Go
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Year : 2015 Volume : 7
Issue 4
Table of contents
IEEE Transactions on Computational Intelligence and AI in Games publication information
A Panorama of Artificial and Computational Intelligence in Games
Temporal Game Challenge Tailoring
Multiobjective Monte Carlo Tree Search for Real-Time Games
Distributed Monte Carlo Tree Search: A Novel Technique and its Application to Computer Go
Automated Planning and Player Modeling for Interactive Storytelling
Introducing IEEE Collabratec
IEEE Access
IEEE Computational Intelligence Society Information
IEEE Transactions on Computational Intelligence and AI in Games information for authors
Issue 3
Issue 2
Year : 2014 Volume : 6
Year : 2013 Volume : 5
Year : 2012 Volume : 4
Year : 2011 Volume : 3
Year : 2010 Volume : 2
Year : 2009 Volume : 1

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Distributed Monte Carlo Tree Search: A Novel Technique and its Application to Computer Go

Content Provider IEEE Xplore Digital Library
Author Schaefers, L. Platzner, M.
Copyright Year 2009
Abstract Monte Carlo tree search (MCTS) has brought about great success regarding the evaluation of stochastic and deterministic games in recent years. We present and empirically analyze a data-driven parallelization approach for MCTS targeting large HPC clusters with Infiniband interconnect. Our implementation is based on OpenMPI and makes extensive use of its RDMA based asynchronous tiny message communication capabilities for effectively overlapping communication and computation. We integrate our parallel MCTS approach termed UCT-Treesplit in our state-of-the-art Go engine Gomorra and measure its strengths and limitations in a real-world setting. Our extensive experiments show that we can scale up to 128 compute nodes and 2048 cores in self-play experiments and, furthermore, give promising directions for additional improvement. The generality of our parallelization approach advocates its use to significantly improve the search quality of a huge number of current MCTS applications.
Starting Page 361
Ending Page 374
Page Count 14
File Size 1815567
File Format PDF
ISSN 1943068X
Volume Number 7
Issue Number 4
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2015-01-01
Publisher Place U.S.A.
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Games Computational modeling Memory management Computers Monte Carlo methods Indexes Data models UCT Distributed memory Go HPC Monte Carlo tree search
Content Type Text
Resource Type Article
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