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A Variated Monte Carlo Tree Search Algorithm for Automatic Performance Tuning to Achieve Load Scalability in InnoDB Storage Engines

Allan Odhiambo Omondi, Ismail Ateya Lukandu, Gregory Wabuke Wanyembi
2019 Zenodo  
A Monte Carlo Tree Search with variated selection, expansion, and simulation stages was used as the core of the designed algorithm.  ...  The measure of effectiveness of the reconfiguration is based on the transaction throughput and response-time latency as the server strives towards achieving load scalability.  ...  The following Sections describe the stages of the Monte Carlo Tree Search with a variation at the selection stage.  ... 
doi:10.5281/zenodo.2558084 fatcat:2cwmibz3gvcyxjphgp74y3pqdu

A New Method for Parallel Monte Carlo Tree Search [article]

S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik, Jos Vermaseren
2016 arXiv   pre-print
This paper proposes a new method for parallel Monte Carlo tree search based on the pipeline computation pattern.  ...  In recent years there has been much interest in the Monte Carlo tree search algorithm, a new, adaptive, randomized optimization algorithm.  ...  CONCLUSION In this paper, we proposed a new method to parallelize Monte Carlo Tree Search by utilizing pipeline pattern.  ... 
arXiv:1605.04447v1 fatcat:5b3cjzsddvcklpdleq3fysx5fa

Scaling link-based similarity search

Dániel Fogaras, Balázs Rácz
2005 Proceedings of the 14th international conference on World Wide Web - WWW '05  
Our methods are presented in a general framework of Monte Carlo similarity search algorithms that precompute an index database of random fingerprints, and at query time, similarities are estimated from  ...  To exploit the similarity information hidden in the hyperlink structure of the web, this paper introduces algorithms scalable to graphs with billions of vertices on a distributed architecture.  ...  The main concept of Monte Carlo similarity search already arises in this example.  ... 
doi:10.1145/1060745.1060839 dblp:conf/www/FogarasR05 fatcat:4i2242pw2rd53lrsr4q7i3l5wm

Towards Large Scale Ad-hoc Teamwork

Elnaz Shafipour Yourdshahi, Thomas Pinder, Gauri Dhawan, Leandro Soriano Marcolino, Plamen Angelov
2018 2018 IEEE International Conference on Agents (ICA)  
Previous works employ Monte Carlo Tree Search approaches. However, the search tree increases exponentially with the number of agents, and only scenarios with very small team sizes have been explored.  ...  Hence, in this paper we propose a history-based version of UCT Monte Carlo Tree Search, using a more compact representation than the original algorithm.  ...  Monte-Carlo Tree Search Although Albrecht and Stone (2017) did not explicitly formalize the ad-hoc teamwork problem as a MDP [6] , they employed a traditional UCT Monte Carlo Tree Search [11] (which  ... 
doi:10.1109/agents.2018.8460136 fatcat:i6dtyyprn5cmbisdqlhrk35cx4

Large-Scale Parallel Monte Carlo Tree Search on GPU

Kamil Rocki, Reiji Suda
2011 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum  
Monte Carlo Tree Search (MCTS) is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games.  ...  It combines the generality of random simulation with the precision of tree search.  ...  INTRODUCTION Monte Carlo Tree Search (MCTS) [1] [2] is a method for making optimal decisions in artificial intelligence (AI) problems, typically move planning in combinatorial games.  ... 
doi:10.1109/ipdps.2011.370 dblp:conf/ipps/RockiS11 fatcat:zqnpuk47rrg4joasfoetnjfe24

Binary Interval Search (BITS): A Scalable Algorithm for Counting Interval Intersections [article]

Ryan M. Layer, Kevin Skadron, Gabriel Robins, Ira M. Hall, Aaron R. Quinlan
2012 arXiv   pre-print
Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection.  ...  Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring  ...  The efficiency of BITS for Monte Carlo applications on GPU architectures provides a scalable platform for identifying novel relationships between large scale genomic datasets.  ... 
arXiv:1208.3407v2 fatcat:2rqrvr7l6bdrrmffyhihqgjcoy

Machine Learning-Based Experimental Design in Materials Science [chapter]

Thaer M. Dieb, Koji Tsuda
2018 Nanoinformatics  
In this chapter, we introduce two machine learningbased approaches for OED: Bayesian optimization (BO) and Monte Carlo tree search (MCTS).  ...  A new approach using Monte Carlo tree search (MCTS) has emerged with competitive search efficiency and superior scalability.  ...  Monte Carlo Tree Search Large-scale problems are not rare cases in materials design and discovery.  ... 
doi:10.1007/978-981-10-7617-6_4 fatcat:25mcb5ngc5bs5l5wv3m3u6hlye

Binary Interval Search: a scalable algorithm for counting interval intersections

Ryan M. Layer, Kevin Skadron, Gabriel Robins, Ira M. Hall, Aaron R. Quinlan
2012 Computer applications in the biosciences : CABIOS  
Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection.  ...  Moreover, we show that BITS is intrinsically suited to parallel computing architectures such as Graphics Processing Units (GPUs) by illustrating its utility for efficient Monte-Carlo simulations measuring  ...  The efficiency of BITS for Monte Carlo applications on GPU architectures provides a scalable platform for identifying novel relationships between large scale genomic datasets.  ... 
doi:10.1093/bioinformatics/bts652 pmid:23129298 pmcid:PMC3530906 fatcat:ajclnjhvozf4jkp6jfuds7ztki

PACHI: State of the Art Open Source Go Program [chapter]

Petr Baudiš, Jean-loup Gailly
2012 Lecture Notes in Computer Science  
We present a state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go.  ...  Monte Carlo Tree Search To evaluate moves, Pachi uses a variant of the Monte Carlo Tree Search (MCTS) -an algorithm based on an incrementally built probabilistic minimax tree.  ...  Programs based on the Monte Carlo Tree Search (MCTS) algorithm and the RAVE variant in particular have enjoyed great success in the recent years.  ... 
doi:10.1007/978-3-642-31866-5_3 fatcat:d43lijwolrhohivuvlj5hcpakq

Monte Carlo Tree Search for Bayesian Reinforcement Learning

Ngo Anh Vien, Wolfgang Ertel
2012 2012 11th International Conference on Machine Learning and Applications  
Keywords Bayesian reinforcement learning · Model-based reinforcement learning · Monte-Carlo tree search · POMDP  ...  In this paper, we examine the use of an online Monte-Carlo tree search (MCTS) algorithm for large POMDPs, to solve the Bayesian reinforcement learning problem online.  ...  Partially observable Monte-Carlo planning (POMCP) The POMCP algorithm [29] is an online planning method that extends the Monte-Carlo tree search (MTCS) method [20] to POMDPs.  ... 
doi:10.1109/icmla.2012.30 dblp:conf/icmla/VienE12 fatcat:au3g7ijkz5cgzoshg2mep6vu34

Monte-Carlo tree search for Bayesian reinforcement learning

Ngo Anh Vien, Wolfgang Ertel, Viet-Hung Dang, TaeChoong Chung
2013 Applied intelligence (Boston)  
Keywords Bayesian reinforcement learning · Model-based reinforcement learning · Monte-Carlo tree search · POMDP  ...  In this paper, we examine the use of an online Monte-Carlo tree search (MCTS) algorithm for large POMDPs, to solve the Bayesian reinforcement learning problem online.  ...  Partially observable Monte-Carlo planning (POMCP) The POMCP algorithm [29] is an online planning method that extends the Monte-Carlo tree search (MTCS) method [20] to POMDPs.  ... 
doi:10.1007/s10489-012-0416-2 fatcat:3p3duvqfsrcopo5im3q3gbdpuq

Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks [article]

Seydou Ba, Takuya Hiraoka, Takashi Onishi, Toru Nakata, Yoshimasa Tsuruoka
2018 arXiv   pre-print
Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions.  ...  For an effective action selection, MCTS performs many simulations to build a reliable tree representation of the decision space.  ...  arXiv:1809.02378v1 [cs.AI] 7 Sep 2018 Background Monte Carlo Tree Search ).  ... 
arXiv:1809.02378v1 fatcat:rvgm44l76zcb5ocugazf6ihpum

Parallel Monte-Carlo Tree Search for HPC Systems [chapter]

Tobias Graf, Ulf Lorenz, Marco Platzner, Lars Schaefers
2011 Lecture Notes in Computer Science  
Monte-Carlo Tree Search (MCTS) is a simulation-based search method that brought about great success to applications such as Computer-Go in the past few years.  ...  In this paper, we present a novel approach for the parallelization of MCTS which allows for an equally distributed spreading of both the work and memory load among all compute nodes within a distributed  ...  Introduction Monte-Carlo tree search (MCTS) is a simulation-based search method that brought about great success in the past few years regarding the evaluation of stochastic and deterministic two-player  ... 
doi:10.1007/978-3-642-23397-5_36 fatcat:z5wfwg5tirfhhcsoacyyivdure

Monte-Carlo tree search and rapid action value estimation in computer Go

Sylvain Gelly, David Silver
2011 Artificial Intelligence  
Monte-Carlo tree search Monte-Carlo tree search (MCTS) uses Monte-Carlo simulation to evaluate the nodes of a search tree [1] .  ...  Monte-Carlo tree search in Go Monte-Carlo tree search was first introduced in the Go program Crazy Stone [1] .  ... 
doi:10.1016/j.artint.2011.03.007 fatcat:wcua7tlf6vc5tjoe2eb7i735mq

Simulation of hyper-inverse Wishart distributions in graphical models

C. M. Carvalho, H. Massam, M. West
2007 Biometrika  
We discuss and investigate questions of scalability of the simulation methods to higher dimensional distributions.  ...  The paper concludes with general comments about the approach, including its use in connection with existing Markov chain Monte Carlo methods that deal with uncertainty about the graphical model structure  ...  Jones et al. (2005) develop, and provide software for, stochastic search and Markov chain Monte Carlo methods to explore posterior distributions over graphs.  ... 
doi:10.1093/biomet/asm056 fatcat:m2ftjgyxjffvro6cbfw25rwula
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