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Multilabeled Value Networks for Computer Go

https://doi.org/10.1109/tg.2018.2852806
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11871842_29openalex-snapshot
Bandit Based Monte-Carlo Planning
inferredcites →https://doi.org/10.1007/978-3-319-50935-8_2openalex-snapshot
Using Deep Convolutional Neural Networks in Monte Carlo Tree Search
inferredcites →https://doi.org/10.1007/978-3-540-75538-8_7openalex-snapshot
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
inferredcites →https://doi.org/10.1007/978-3-642-31866-5_3openalex-snapshot
PACHI: State of the Art Open Source Go Program
inferredcites →https://doi.org/10.1007/bf00992696openalex-snapshot
Simple statistical gradient-following algorithms for connectionist reinforcement learning
inferredcites →https://doi.org/10.1007/s10472-011-9258-6openalex-snapshot
Multi-armed bandits with episode context
inferredcites →https://doi.org/10.1016/s0004-3702(01)00121-7openalex-snapshot
Computer Go
inferredcites →https://doi.org/10.1016/s0004-3702(01)00152-7openalex-snapshot
Games solved: Now and in the future
inferredcites →https://doi.org/10.1023/a:1013689704352openalex-snapshot
Finite-time Analysis of the Multiarmed Bandit Problem
inferredcites →https://doi.org/10.1038/nature14236openalex-snapshot
Human-level control through deep reinforcement learning
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/nature16961openalex-snapshot
Mastering the game of Go with deep neural networks and tree search
inferredcites →https://doi.org/10.1109/tciaig.2010.2083662openalex-snapshot
Fuego—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search
inferredcites →https://doi.org/10.1109/tciaig.2012.2186810openalex-snapshot
A Survey of Monte Carlo Tree Search Methods
inferredcites →https://doi.org/10.1109/tciaig.2017.2681042openalex-snapshot
Residual Networks for Computer Go
inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1109/tnn.2004.842673openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1145/1273496.1273531openalex-snapshot
Combining online and offline knowledge in UCT
inferredcites →https://doi.org/10.1145/2093548.2093574openalex-snapshot
The grand challenge of computer Go
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.26481/dis.19940923laopenalex-snapshot
Searching for solutions in games and artificial intelligence
inferredcites →https://doi.org/10.3233/icg-2007-30403openalex-snapshot
COMPUTING “ELO RATINGS” OF MOVE PATTERNS IN THE GAME OF GO1
inferredcites →https://doi.org/10.3233/icg-2011-34302openalex-snapshot
Balancing MCTS by Dynamically Adjusting the Komi Value
inferredcites →https://doi.org/10.48550/arxiv.1412.6564openalex-snapshot
Move Evaluation in Go Using Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2501.05407openalex-snapshot
On-line Policy Improvement using Monte-Carlo Search

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3893835openalex-extracted
How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program
inferred← citeshttps://doi.org/10.1007/978-3-031-56855-8_23openalex-snapshot
Vision Transformers for Computer Go
inferred← citeshttps://doi.org/10.1007/s10462-022-10228-yopenalex-snapshot
Monte Carlo Tree Search: a review of recent modifications and applications
inferred← citeshttps://doi.org/10.1016/j.aej.2024.07.125openalex-snapshot
Fairness mechanism and stackelberg strategy for multi-agent systems: A case study of legends of the three kingdoms
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112455openalex-snapshot
End-to-end Go (Weiqi) game record reconstruction from live broadcast videos
inferred← citeshttps://doi.org/10.1016/j.entcom.2024.100894openalex-snapshot
A concise review of intelligent game agent
inferred← citeshttps://doi.org/10.1093/pnasnexus/pgaf138openalex-snapshot
The dual edges of AI: Advancing knowledge while reducing diversity
inferred← citeshttps://doi.org/10.1109/mci.2020.2998315openalex-snapshot
Strength Adjustment and Assessment for MCTS-Based Programs [Research Frontier]
inferred← citeshttps://doi.org/10.1109/tg.2025.3648019openalex-snapshot
A Study of Solving Life-and-Death Problems in Go Using Relevance-Zone-Based Solvers
inferred← citeshttps://doi.org/10.1145/3644116.3644301openalex-snapshot
The Full Intelligent Stakeholder Value Network Model and Its Empirical Results Based on Value Stream in Hospital
inferred← citeshttps://doi.org/10.1609/aaai.v33i01.33011222openalex-snapshot
On Strength Adjustment for MCTS-Based Programs
inferred← citeshttps://doi.org/10.1609/aaai.v34i01.5454openalex-snapshot
Accelerating and Improving AlphaZero Using Population Based Training
inferred← citeshttps://doi.org/10.3390/electronics13153093openalex-snapshot
Streamlined Deep Learning Models for Move Prediction in Go-Game
inferred← citeshttps://doi.org/10.3390/info12090352openalex-snapshot
What Constitutes Fairness in Games? A Case Study with Scrabble
inferred← citeshttps://doi.org/10.48550/arxiv.1902.10565openalex-snapshot
Accelerating Self-Play Learning in Go
inferred← citeshttps://doi.org/10.48550/arxiv.1905.10863openalex-snapshot
SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)
inferred← citeshttps://doi.org/10.54254/2755-2721/6/20230845openalex-snapshot
Machine learning algorithm and training in Go—Take three influential program as example
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