[38;5;12m                                                                                                  [39m[38;2;255;187;0m[1m[4mAwesome Monte Carlo Tree Search Papers.[0m
[38;5;14m[1m![0m[38;5;12mAwesome[39m[38;5;14m[1m [0m[38;5;14m[1m(https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)[0m[38;5;12m [39m[38;5;12m(https://github.com/sindresorhus/awesome)[39m[38;5;12m [39m[38;5;14m[1m![0m[38;5;12mPRs[39m[38;5;12m [39m[38;5;12mWelcome[39m[38;5;14m[1m [0m
[38;5;14m[1m(https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)[0m[38;5;12m [39m[38;5;12m(http://makeapullrequest.com)[39m[38;5;14m[1m![0m[38;5;12mrepo[39m[38;5;12m [39m[38;5;12msize[39m[38;5;14m[1m [0m[38;5;14m[1m(https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-monte-carlo-tree-search-papers.svg)[0m[38;5;12m [39m
[38;5;12m(https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers/archive/master.zip)[39m[38;5;12m [39m[38;5;12m![39m[38;5;14m[1mLicense[0m[38;5;12m [39m[38;5;12m(https://img.shields.io/github/license/benedekrozemberczki/awesome-tree-search-papers.svg?color=blue)[39m[38;5;12m [39m[38;5;14m[1m![0m[38;5;12mbenedekrozemberczki[39m[38;5;14m[1m [0m
[38;5;14m[1m(https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)[0m[38;5;12m [39m[38;5;12m(https://twitter.com/intent/follow?screen_name=benrozemberczki)[39m[38;5;12m [39m

[38;5;12m  [39m


[38;5;238m――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――[39m

[38;5;12mA curated list of Monte Carlo tree search papers with implementations from the following conferences/journals:[39m

[38;5;12m- Machine learning[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mNeurIPS[0m[38;5;12m (https://nips.cc/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICML[0m[38;5;12m (https://icml.cc/)[39m
[38;5;12m- Computer vision[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCVPR[0m[38;5;12m (http://cvpr2019.thecvf.com/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mICCV[0m[38;5;12m (http://iccv2019.thecvf.com/)[39m
[38;5;12m- Natural language processing[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mACL[0m[38;5;12m (http://www.acl2019.org/EN/index.xhtml)[39m
[38;5;12m- Data[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mKDD[0m[38;5;12m (https://www.kdd.org/)[39m
[38;5;12m- Artificial intelligence[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAAAI[0m[38;5;12m (https://www.aaai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mAISTATS[0m[38;5;12m (https://www.aistats.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mIJCAI[0m[38;5;12m (https://www.ijcai.org/)[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mUAI[0m[38;5;12m (http://www.auai.org/)[39m
[38;5;12m- Robotics[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mRAS[0m[38;5;12m (https://www.journals.elsevier.com/robotics-and-autonomous-systems)[39m
[38;5;12m- Games[39m
[38;5;12m [39m[38;5;12m [39m[38;5;12m [39m[48;5;12m[38;5;11m⟡[49m[39m[38;5;12m [39m[38;5;14m[1mCIG[0m[38;5;12m (http://www.ieee-cig.org/)[39m

[38;5;12mSimilar[39m[38;5;12m [39m[38;5;12mcollections[39m[38;5;12m [39m[38;5;12mabout[39m[38;5;12m [39m[38;5;14m[1mgraph[0m[38;5;14m[1m [0m[38;5;14m[1mclassification[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-graph-classification),[39m[38;5;12m [39m[38;5;14m[1mgradient[0m[38;5;14m[1m [0m[38;5;14m[1mboosting[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers),[39m[38;5;12m [39m[38;5;14m[1mclassification/regression[0m
[38;5;14m[1mtrees[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-decision-tree-papers),[39m[38;5;12m [39m[38;5;14m[1mfraud[0m[38;5;14m[1m [0m[38;5;14m[1mdetection[0m[38;5;12m [39m[38;5;12m(https://github.com/benedekrozemberczki/awesome-fraud-detection-papers),[39m[38;5;12m [39m[38;5;12mand[39m[38;5;12m [39m[38;5;14m[1mcommunity[0m[38;5;14m[1m [0m[38;5;14m[1mdetection[0m[38;5;12m [39m
[38;5;12m(https://github.com/benedekrozemberczki/awesome-community-detection)[39m[38;5;12m [39m[38;5;12mpapers[39m[38;5;12m [39m[38;5;12mwith[39m[38;5;12m [39m[38;5;12mimplementations.[39m

[38;2;255;187;0m[4m2023[0m
[38;5;12m- [39m[38;5;14m[1mSymbolic Physics Learner: Discovering governing equations via Monte Carlo tree search (ICLR 2023)[0m
[38;5;12m  - Fangzheng Sun, Yang Liu, Jian-Xun Wang, Hao Sun[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2205.13134)[39m

[38;2;255;187;0m[4m2022[0m
[38;5;12m- [39m[38;5;14m[1mFinding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework (AAAI 2022)[0m
[38;5;12m  - Elias B. Khalil, Pashootan Vaezipoor, Bistra Dilkina[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2110.08423)[39m

[38;5;12m- [39m[38;5;14m[1mNSGZero: Efficiently Learning Non-exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search (AAAI 2022)[0m
[38;5;12m  - Wanqi Xue, Bo An, Chai Kiat Yeo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2201.07224)[39m

[38;5;12m- [39m[38;5;14m[1mSolving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks (AAAI 2022)[0m
[38;5;12m  - Kevin Osanlou, Jeremy Frank, Andrei Bursuc, Tristan Cazenave, Eric Jacopin, Christophe Guettier, J. Benton[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/21224)[39m

[38;5;12m- [39m[38;5;14m[1mQubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search (AAAI 2022)[0m
[38;5;12m  - Animesh Sinha, Utkarsh Azad, Harjinder Singh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2104.01992)[39m

[38;5;12m- [39m[38;5;14m[1mSplit Moves for Monte-Carlo Tree Search (AAAI 2022)[0m
[38;5;12m  - Jakub Kowalski, Maksymilian Mika, Wojciech Pawlik, Jakub Sutowicz, Marek Szykula, Mark H. M. Winands[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2112.07761)[39m

[38;5;12m- [39m[38;5;14m[1mProcrastinated Tree Search: Black-Box Optimization with Delayed%2C Noisy and Multi-Fidelity Feedback (AAAI 2022)[0m
[38;5;12m  - Junxiong Wang, Debabrota Basu, Immanuel Trummer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2110.07232)[39m

[38;5;12m- [39m[38;5;14m[1mEnabling Arbitrary Translation Objectives with Adaptive Tree Search (ICLR 2022)[0m
[38;5;12m  - Wang Ling, Wojciech Stokowiec, Domenic Donato, Chris Dyer, Lei Yu, Laurent Sartran, Austin Matthews[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://en.x-mol.com/paper/article/1496885785571840000)[39m

[38;5;12m- [39m[38;5;14m[1mWhat's Wrong with Deep Learning in Tree Search for Combinatorial Optimization (ICLR 2022)[0m
[38;5;12m  - Maximili1an Böther, Otto Kißig, Martin Taraz, Sarel Cohen, Karen Seidel, Tobias Friedrich[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2201.10494)[39m

[38;5;12m- [39m[38;5;14m[1mAnytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search (IJCAI 2022)[0m
[38;5;12m  - Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2022/1)[39m

[38;5;12m- [39m[38;5;14m[1mFast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search (RECSYS 2022)[0m
[38;5;12m  - Dilina Chandika Rajapakse, Douglas Leith[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.scss.tcd.ie/Doug.Leith/pubs/recsys22-35.pdf)[39m

[38;2;255;187;0m[4m2021[0m

[38;5;12m- [39m[38;5;14m[1mLearning to Stop: Dynamic Simulation Monte-Carlo Tree Search (AAAI 2021)[0m
[38;5;12m  - Li-Cheng Lan, Ti-Rong Wu, I-Chen Wu, Cho-Jui Hsieh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2012.07910)[39m

[38;5;12m- [39m[38;5;14m[1mDec-SGTS: Decentralized Sub-Goal Tree Search for Multi-Agent Coordination (AAAI 2021)[0m
[38;5;12m  - Minglong Li, Zhongxuan Cai, Wenjing Yang, Lixia Wu, Yinghui Xu, Ji Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/17345)[39m

[38;5;12m- [39m[38;5;14m[1mImproved POMDP Tree Search Planning with Prioritized Action Branching (AAAI 2021)[0m
[38;5;12m  - John Mern, Anil Yildiz, Lawrence Bush, Tapan Mukerji, Mykel J. Kochenderfer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2010.03599)[39m

[38;5;12m- [39m[38;5;14m[1mDynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search (AAAI 2021)[0m
[38;5;12m  - Alvaro Velasquez, Brett Bissey, Lior Barak, Andre Beckus, Ismail Alkhouri, Daniel Melcer, George K. Atia[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/17427)[39m

[38;5;12m- [39m[38;5;14m[1mSingle Player Monte-Carlo Tree Search Based on the Plackett-Luce Model (AAAI 2021)[0m
[38;5;12m  - Felix Mohr, Viktor Bengs, Eyke Hüllermeier[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ojs.aaai.org/index.php/AAAI/article/view/17468)[39m

[38;5;12m- [39m[38;5;14m[1mLearning to Pack: A Data-Driven Tree Search Algorithm for Large-Scale 3D Bin Packing Problem (CIKM 2021)[0m
[38;5;12m  - Qianwen Zhu, Xihan Li, Zihan Zhang, Zhixing Luo, Xialiang Tong, Mingxuan Yuan, Jia Zeng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3459637.3481933)[39m

[38;5;12m- [39m[38;5;14m[1mPractical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design (ICLR 2021)[0m
[38;5;12m  - Xiufeng Yang, Tanuj Kr Aasawat, Kazuki Yoshizoe[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2006.10504)[39m

[38;5;12m- [39m[38;5;14m[1mConvex Regularization in Monte-Carlo Tree Search (ICML 2021)[0m
[38;5;12m  - Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2007.00391)[39m

[38;5;12m- [39m[38;5;14m[1mCombining Tree Search and Action Prediction for State-of-the-Art Performance in DouDiZhu (IJCAI 2021)[0m
[38;5;12m  - Yunsheng Zhang, Dong Yan, Bei Shi, Haobo Fu, Qiang Fu, Hang Su, Jun Zhu, Ning Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2021/470)[39m

[38;2;255;187;0m[4m2020[0m

[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds (AAAI 2020)[0m
[38;5;12m  - Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Pack Kaelbling, Tomás Lozano-Pérez[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/Papers/AAAI/2020GB/AAAI-KimB.1282.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mNeural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search (AAAI 2020)[0m
[38;5;12m  - Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1805.07440)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/linnanwang/AlphaX-NASBench101)[39m

[38;5;12m- [39m[38;5;14m[1mMonte-Carlo Tree Search in Continuous Action Spaces with Value Gradients (AAAI 2020)[0m
[38;5;12m  - Jongmin Lee, Wonseok Jeon, Geon-Hyeong Kim, Kee-Eung Kim[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/16/Papers/104.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/leekwoon/KR-DL-UCT)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mApproximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions (AISTATS 2020)[0m
[38;5;12m  - Lars Buesing, Nicolas Heess, Theophane Weber[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1910.06862)[39m

[38;5;12m- [39m[38;5;14m[1mWatch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search (ICLR 2020)[0m
[38;5;12m  - Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://openreview.net/forum?id=BJlQtJSKDB)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/brilee/python_uct)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mInformation Particle Filter Tree: An Online Algorithm for POMDPs with Belief-Based Rewards on Continuous Domains (ICML 2020)[0m
[38;5;12m  - Johannes Fischer, Ömer Sahin Tas[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v119/fischer20a.html)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/johannes-fischer/icml2020_ipft)[39m

[38;5;12m- [39m[38;5;14m[1mSub-Goal Trees a Framework for Goal-Based Reinforcement Learning (ICML 2020)[0m
[38;5;12m  - Tom Jurgenson, Or Avner, Edward Groshev, Aviv Tamar[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/2002.12361)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mMonte-Carlo Tree Search for Scalable Coalition Formation (IJCAI 2020)[0m
[38;5;12m  - Feng Wu, Sarvapali D. Ramchurn[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/2020/57)[39m

[38;5;12m- [39m[38;5;14m[1mGeneralized Mean Estimation in Monte-Carlo Tree Search (IJCAI 2020)[0m
[38;5;12m  - Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1911.00384)[39m

[38;5;12m- [39m[38;5;14m[1mSparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces (IJCAI 2020)[0m
[38;5;12m  - Michael H. Lim, Claire Tomlin, Zachary N. Sunberg[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1910.04332)[39m
[38;5;12m  [39m
[38;5;12m- [39m[38;5;14m[1mMix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions (NeurIPS 2020)[0m
[38;5;12m  - Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1907.10154)[39m

[38;5;12m- [39m[38;5;14m[1mExtracting Knowledge from Web Text with Monte Carlo Tree Search (WWW 2020)[0m
[38;5;12m  - Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/doi/abs/10.1145/3366423.3380010)[39m

[38;2;255;187;0m[4m2019[0m
[38;5;12m- [39m[38;5;14m[1mACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search (AAAI 2019)[0m
[38;5;12m  - Shangtong Zhang, Hengshuai Yao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1811.02696)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/ShangtongZhang/DeepRL)[39m

[38;5;12m- [39m[38;5;14m[1mA Monte Carlo Tree Search Player for Birds of a Feather Solitaire (AAAI 2019)[0m
[38;5;12m  - Christian Roberson, Katarina Sperduto[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ojs/index.php/AAAI/article/view/5036)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (http://cs.gettysburg.edu/~tneller/puzzles/boaf/)[39m

[38;5;12m- [39m[38;5;14m[1mVine Copula Structure Learning via Monte Carlo Tree Search (AISTATS 2019)[0m
[38;5;12m  - Bo Chang, Shenyi Pan, Harry Joe[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://proceedings.mlr.press/v89/chang19a/chang19a.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/changebo/Vine_MCTS)[39m

[38;5;12m- [39m[38;5;14m[1mNoisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach (AISTATS 2019)[0m
[38;5;12m  - Rajat Sen, Kirthevasan Kandasamy, Sanjay Shakkottai[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1810.10482)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/rajatsen91/MFTREE_DET)[39m

[38;5;12m- [39m[38;5;14m[1mReinforcement Learning Based Monte Carlo Tree Search for Temporal Path Discovery (ICDM 2019)[0m
[38;5;12m  - Pengfei Ding, Guanfeng Liu, Pengpeng Zhao, An Liu, Zhixu Li, Kai Zheng[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://zheng-kai.com/paper/icdm_2019_b.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search for Policy Optimization (IJCAI 2019)[0m
[38;5;12m  - Xiaobai Ma, Katherine Rose Driggs-Campbell, Zongzhang Zhang, Mykel J. Kochenderfer[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0432.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mSubgoal-Based Temporal Abstraction in Monte-Carlo Tree Search (IJCAI 2019)[0m
[38;5;12m  - Thomas Gabor, Jan Peter, Thomy Phan, Christian Meyer, Claudia Linnhoff-Popien[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0772.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/jnptr/subgoal-mcts)[39m

[38;5;12m- [39m[38;5;14m[1mAutomated Machine Learning with Monte-Carlo Tree Search (IJCAI 2019)[0m
[38;5;12m  - Herilalaina Rakotoarison, Marc Schoenauer, Michèle Sebag[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0457.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/herilalaina/mosaic_ml)[39m

[38;5;12m- [39m[38;5;14m[1mMultiple Policy Value Monte Carlo Tree Search (IJCAI 2019)[0m
[38;5;12m  - Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2019/0653.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Compositional Neural Programs with Recursive Tree Search and Planning (NeurIPS 2019)[0m
[38;5;12m  - Thomas Pierrot, Guillaume Ligner, Scott E. Reed, Olivier Sigaud, Nicolas Perrin, Alexandre Laterre, David Kas, Karim Beguir, Nando de Freitas[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1905.12941)[39m

[38;2;255;187;0m[4m2018[0m
[38;5;12m- [39m[38;5;14m[1mMonte Carlo Methods for the Game Kingdomino (CIG 2018)[0m
[38;5;12m  - Magnus Gedda, Mikael Z. Lagerkvist, Martin Butler[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1807.04458)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mgedda/kdom-ai)[39m
[38;5;12m  - [39m[38;5;12mGame Server[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/mratin/kdom)[39m

[38;5;12m- [39m[38;5;14m[1mReset-free Trial-and-Error Learning for Robot Damage Recovery (RAS 2018)[0m
[38;5;12m  - Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Jean-Baptiste Mouret[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1610.04213.pdf)[39m
[38;5;12m  - [39m[38;5;12mCode[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/resibots/chatzilygeroudis_2018_rte)[39m
[38;5;12m  - [39m[38;5;12mMCTS C++ Library[39m[38;5;14m[1m [0m[38;5;12m (https://github.com/resibots/mcts)[39m

[38;5;12m- [39m[38;5;14m[1mMemory-Augmented Monte Carlo Tree Search (AAAI 2018)[0m
[38;5;12m  - Chenjun Xiao, Jincheng Mei, Martin Müller[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17139)[39m

[38;5;12m- [39m[38;5;14m[1mFeedback-Based Tree Search for Reinforcement Learning (ICML 2018)[0m
[38;5;12m  - Daniel R. Jiang, Emmanuel Ekwedike, Han Liu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1805.05935)[39m

[38;5;12m- [39m[38;5;14m[1mExtended Increasing Cost Tree Search for Non-Unit Cost Domains (IJCAI 2018)[0m
[38;5;12m  - Thayne T. Walker, Nathan R. Sturtevant, Ariel Felner[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2018/74)[39m

[38;5;12m- [39m[38;5;14m[1mThree-Head Neural Network Architecture for Monte Carlo Tree Search (IJCAI 2018)[0m
[38;5;12m  - Chao Gao, Martin Müller, Ryan Hayward[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2018/523)[39m

[38;5;12m- [39m[38;5;14m[1mBidding in Periodic Double Auctions Using Heuristics and Dynamic Monte Carlo Tree Search (IJCAI 2018)[0m
[38;5;12m  - Moinul Morshed Porag Chowdhury, Christopher Kiekintveld, Son Tran, William Yeoh[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2018/23)[39m

[38;5;12m- [39m[38;5;14m[1mCombinatorial Optimization with Graph Convolutional Networks and Guided Tree Search (NIPS 2018)[0m
[38;5;12m  - Zhuwen Li, Qifeng Chen, Vladlen Koltun[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1810.10659)[39m

[38;5;12m- [39m[38;5;14m[1mM-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search (NIPS 2018)[0m
[38;5;12m  - Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1802.04394)[39m

[38;5;12m- [39m[38;5;14m[1mSingle-Agent Policy Tree Search With Guarantees (NIPS 2018)[0m
[38;5;12m  - Laurent Orseau, Levi Lelis, Tor Lattimore, Theophane Weber[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1811.10928)[39m

[38;5;12m- [39m[38;5;14m[1mMonte-Carlo Tree Search for Constrained POMDPs (NIPS 2018)[0m
[38;5;12m  - Jongmin Lee, Geon-hyeong Kim, Pascal Poupart, Kee-Eung Kim[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://cs.uwaterloo.ca/~ppoupart/publications/constrained-pomdps/mcts-constrained-pomdps-paper.pdf)[39m

[38;2;255;187;0m[4m2017[0m
[38;5;12m- [39m[38;5;14m[1mAn Analysis of Monte Carlo Tree Search (AAAI 2017)[0m
[38;5;12m  - Steven James, George Dimitri Konidaris, Benjamin Rosman[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14886)[39m

[38;5;12m- [39m[38;5;14m[1mBeyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation (AAAI 2017)[0m
[38;5;12m  - Jinzhuo Wang, Wenmin Wang, Ronggang Wang, Wen Gao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1706.04052)[39m

[38;5;12m- [39m[38;5;14m[1mDesigning Better Playlists with Monte Carlo Tree Search (AAAI 2017)[0m
[38;5;12m  - Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.utexas.edu/~pstone/Papers/bib2html-links/IAAI2017-eladlieb.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLearning in POMDPs with Monte Carlo Tree Search (ICML 2017)[0m
[38;5;12m  - Sammie Katt, Frans A. Oliehoek, Christopher Amato[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1806.05631)[39m

[38;5;12m- [39m[38;5;14m[1mLearning to Run Heuristics in Tree Search (IJCAI 2017)[0m
[38;5;12m  - Elias B. Khalil, Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, Yufen Shao[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2017/92)[39m

[38;5;12m- [39m[38;5;14m[1mEstimating the Size of Search Trees by Sampling with Domain Knowledge (IJCAI 2017)[0m
[38;5;12m  - Gleb Belov, Samuel Esler, Dylan Fernando, Pierre Le Bodic, George L. Nemhauser[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2017/67)[39m

[38;5;12m- [39m[38;5;14m[1mA Monte Carlo Tree Search Approach to Active Malware Analysis (IJCAI 2017)[0m
[38;5;12m  - Riccardo Sartea, Alessandro Farinelli[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/proceedings/2017/535)[39m

[38;5;12m- [39m[38;5;14m[1mMonte-Carlo Tree Search by Best Arm Identification (NIPS 2017)[0m
[38;5;12m  - Emilie Kaufmann, Wouter M. Koolen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1706.02986)[39m

[38;5;12m- [39m[38;5;14m[1mThinking Fast and Slow with Deep Learning and Tree Search (NIPS 2017)[0m
[38;5;12m  - Thomas Anthony, Zheng Tian, David Barber[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1705.08439)[39m

[38;5;12m- [39m[38;5;14m[1mMonte-Carlo Tree Search using Batch Value of Perfect Information (UAI 2017)[0m
[38;5;12m  - Shahaf S. Shperberg, Solomon Eyal Shimony, Ariel Felner[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://auai.org/uai2017/proceedings/papers/37.pdf)[39m

[38;2;255;187;0m[4m2016[0m
[38;5;12m- [39m[38;5;14m[1mUsing Domain Knowledge to Improve Monte-Carlo Tree Search Performance in Parameterized Poker Squares (AAAI 2016)[0m
[38;5;12m  - Robert Arrington, Clay Langley, Steven Bogaerts[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11809)[39m

[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search for Multi-Robot Task Allocation (AAAI 2016)[0m
[38;5;12m  - Bilal Kartal, Ernesto Nunes, Julio Godoy, Maria L. Gini[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12154)[39m

[38;5;12m- [39m[38;5;14m[1mLarge Scale Hard Sample Mining with Monte Carlo Tree Search (CVPR 2016)[0m
[38;5;12m  - Olivier Canévet, François Fleuret[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.idiap.ch/~fleuret/papers/canevet-fleuret-cvpr2016.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mOn the Analysis of Complex Backup Strategies in Monte Carlo Tree Search (ICML 2016)[0m
[38;5;12m  - Piyush Khandelwal, Elad Liebman, Scott Niekum, Peter Stone[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.cs.utexas.edu/~eladlieb/ICML2016.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mDeep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games (IJCAI 2016)[0m
[38;5;12m  - Xiaoxiao Guo, Satinder P. Singh, Richard L. Lewis, Honglak Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1604.07095)[39m

[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty (IJCAI 2016)[0m
[38;5;12m  - Timothy Yee, Viliam Lisý, Michael H. Bowling[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/16/Papers/104.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Predictive State Representations via Monte-Carlo Tree Search (IJCAI 2016)[0m
[38;5;12m  - Yunlong Liu, Hexing Zhu, Yifeng Zeng, Zongxiong Dai[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/8056/df11094fc96d76826403f8b339dc14aa821f.pdf)[39m

[38;2;255;187;0m[4m2015[0m
[38;5;12m- [39m[38;5;14m[1mEfficient Globally Optimal Consensus Maximisation with Tree Search (CVPR 2015)[0m
[38;5;12m  - Tat-Jun Chin, Pulak Purkait, Anders P. Eriksson, David Suter[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://zpascal.net/cvpr2015/Chin_Efficient_Globally_Optimal_2015_CVPR_paper.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mInterplanetary Trajectory Planning with Monte Carlo Tree Search (IJCAI 2015)[0m
[38;5;12m  - Daniel Hennes, Dario Izzo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/ce42/53ca1c5b16e96cdbefae75649cd2588f42f3.pdf)[39m

[38;2;255;187;0m[4m2014[0m
[38;5;12m- [39m[38;5;14m[1mState Aggregation in Monte Carlo Tree Search (AAAI 2014)[0m
[38;5;12m  - Jesse Hostetler, Alan Fern, Tom Dietterich[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8439/8712)[39m

[38;5;12m- [39m[38;5;14m[1mDeep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning (NIPS 2014)[0m
[38;5;12m  - Xiaoxiao Guo, Satinder P. Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://web.eecs.umich.edu/~baveja/Papers/UCTtoCNNsAtariGames-FinalVersion.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Partial Policies to Speedup MDP Tree Search (UAI 2014)[0m
[38;5;12m  - Jervis Pinto, Alan Fern[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www.jmlr.org/papers/volume18/15-251/15-251.pdf)[39m

[38;2;255;187;0m[4m2013[0m
[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search for Scheduling Activity Recognition (ICCV 2013)[0m
[38;5;12m  - Mohamed R. Amer, Sinisa Todorovic, Alan Fern, Song-Chun Zhu[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.405.5916&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mConvergence of Monte Carlo Tree Search in Simultaneous Move Games (NIPS 2013)[0m
[38;5;12m  - Viliam Lisý, Vojtech Kovarík, Marc Lanctot, Branislav Bosanský[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/5145-convergence-of-monte-carlo-tree-search-in-simultaneous-move-games)[39m

[38;5;12m- [39m[38;5;14m[1mBayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search (NIPS 2013)[0m
[38;5;12m  - Aijun Bai, Feng Wu, Xiaoping Chen[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/5111-bayesian-mixture-modelling-and-inference-based-thompson-sampling-in-monte-carlo-tree-search)[39m

[38;2;255;187;0m[4m2012[0m
[38;5;12m- [39m[38;5;14m[1mGeneralized Monte-Carlo Tree Search Extensions for General Game Playing (AAAI 2012)[0m
[38;5;12m  - Hilmar Finnsson[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewFile/4935/5300)[39m

[38;2;255;187;0m[4m2011[0m
[38;5;12m- [39m[38;5;14m[1mA Local Monte Carlo Tree Search Approach in Deterministic Planning (AAAI 2011)[0m
[38;5;12m  - Fan Xie, Hootan Nakhost, Martin Müller[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.699.3833&rep=rep1&type=pdf)[39m

[38;5;12m- [39m[38;5;14m[1mReal-Time Solving of Quantified CSPs Based on Monte-Carlo Game Tree Search (IJCAI 2011)[0m
[38;5;12m  - Satomi Baba, Yongjoon Joe, Atsushi Iwasaki, Makoto Yokoo[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/11/Papers/116.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mNested Rollout Policy Adaptation for Monte Carlo Tree Search (IJCAI 2011)[0m
[38;5;12m  - Christopher D. Rosin[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/11/Papers/115.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mVariance Reduction in Monte-Carlo Tree Search (NIPS 2011)[0m
[38;5;12m  - Joel Veness, Marc Lanctot, Michael H. Bowling[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/4288-variance-reduction-in-monte-carlo-tree-search)[39m

[38;5;12m- [39m[38;5;14m[1mLearning Is Planning: Near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search (UAI 2011)[0m
[38;5;12m  - John Asmuth, Michael L. Littman[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1202.3699)[39m

[38;2;255;187;0m[4m2010[0m
[38;5;12m- [39m[38;5;14m[1mUnderstanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search (AAAI 2010)[0m
[38;5;12m  - Jeffrey Richard Long, Nathan R. Sturtevant, Michael Buro, Timothy Furtak[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://pdfs.semanticscholar.org/011e/2c79575721764c127e210c9d8105a6305e70.pdf)[39m

[38;5;12m- [39m[38;5;14m[1mBayesian Inference in Monte-Carlo Tree Search (UAI 2010)[0m
[38;5;12m  - Gerald Tesauro, V. T. Rajan, Richard Segal[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/1203.3519)[39m

[38;2;255;187;0m[4m2009[0m
[38;5;12m- [39m[38;5;14m[1mMonte Carlo Tree Search Techniques in the Game of Kriegspiel (IJCAI 2009)[0m
[38;5;12m  - Paolo Ciancarini, Gian Piero Favini[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/viewFile/396/693)[39m

[38;5;12m- [39m[38;5;14m[1mBootstrapping from Game Tree Search (NIPS 2009)[0m
[38;5;12m  - Joel Veness, David Silver, William T. B. Uther, Alan Blair[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://papers.nips.cc/paper/3722-bootstrapping-from-game-tree-search)[39m

[38;2;255;187;0m[4m2008[0m
[38;5;12m- [39m[38;5;14m[1mDirect Mining of Discriminative and Essential Frequent Patterns via Model-Based Search Tree (KDD 2008)[0m
[38;5;12m  - Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Yan, Jiawei Han, Philip S. Yu, Olivier Verscheure[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (http://www1.se.cuhk.edu.hk/~hcheng/paper/kdd08mbt.pdf)[39m

[38;2;255;187;0m[4m2007[0m
[38;5;12m- [39m[38;5;14m[1mBandit Algorithms for Tree Search (UAI 2007)[0m
[38;5;12m  - Pierre-Arnaud Coquelin, Rémi Munos[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/pdf/1408.2028.pdf)[39m

[38;2;255;187;0m[4m2006[0m
[38;5;12m- [39m[38;5;14m[1mProperties of Forward Pruning in Game-Tree Search (AAAI 2006)[0m
[38;5;12m  - Yew Jin Lim, Wee Sun Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dl.acm.org/citation.cfm?id=1597351)[39m

[38;5;12m- [39m[38;5;14m[1mGraph Branch Algorithm: An Optimum Tree Search Method for Scored Dependency Graph with Arc Co-Occurrence Constraints (ACL 2006)[0m
[38;5;12m  - Hideki Hirakawa[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.aclweb.org/anthology/P06-2047/)[39m

[38;2;255;187;0m[4m2005[0m
[38;5;12m- [39m[38;5;14m[1mGame-Tree Search with Combinatorially Large Belief States (IJCAI 2005)[0m
[38;5;12m  - Austin Parker, Dana S. Nau, V. S. Subrahmanian[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/05/Papers/0878.pdf)[39m

[38;2;255;187;0m[4m2003[0m
[38;5;12m- [39m[38;5;14m[1mSolving Finite Domain Constraint Hierarchies by Local Consistency and Tree Search (IJCAI 2003)[0m
[38;5;12m  - Stefano Bistarelli, Philippe Codognet, Kin Chuen Hui, Jimmy Ho-Man Lee[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://www.ijcai.org/Proceedings/03/Papers/200.pdf)[39m

[38;2;255;187;0m[4m2001[0m
[38;5;12m- [39m[38;5;14m[1mIncomplete Tree Search using Adaptive Probing (IJCAI 2001)[0m
[38;5;12m  - Wheeler Ruml[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://dash.harvard.edu/bitstream/handle/1/23017275/tr-02-01.pdf?sequence%3D1)[39m

[38;2;255;187;0m[4m1998[0m
[38;5;12m- [39m[38;5;14m[1mKnightCap: A Chess Programm That Learns by Combining TD with Game-Tree Search (ICML 1998)[0m
[38;5;12m  - Jonathan Baxter, Andrew Tridgell, Lex Weaver[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://arxiv.org/abs/cs/9901002)[39m

[38;2;255;187;0m[4m1988[0m
[38;5;12m- [39m[38;5;14m[1mA Tree Search Algorithm for Target Detection in Image Sequences (CVPR 1988)[0m
[38;5;12m  - Steven D. Blostein, Thomas S. Huang[39m
[38;5;12m  - [39m[38;5;12mPaper[39m[38;5;14m[1m [0m[38;5;12m (https://ieeexplore.ieee.org/document/196309)[39m

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[38;5;12m- [39m[38;5;14m[1mCC0 Universal[0m[38;5;12m (https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers/blob/master/LICENSE)[39m

[38;5;238m――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――[39m
