Common fate graph patterns in Monte Carlo Tree Search for computer go

Graf, Tobias; Platzner, Marco

doi:10.1109/cig.2014.6932863

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…The core MCTS program used for the experiments makes use of technologies like RAVE [9], progressive widening [15], progressive bias [6] and a large amount of knowledge (shape and common fate graph patterns [10]) in the tree search part. On the internet server KGS, where computer programs can play against humans, with the inclusion of adaptive playouts it has reached a rank of 3 dan under the name "abakus".…”

Section: Methodsmentioning

confidence: 99%

Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Graf

Platzner

2016

Theoretical Computer Science

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Monte-Carlo Tree Search evaluates positions with the help of a playout policy. If the playout policy evaluates a position wrong then there are cases where the tree search has difficulties to find the correct move due to the large search space. This paper explores adaptive playout policies which improve the playout policy during a tree search. With the help of policy gradient reinforcement learning techniques we optimize the playout policy to give better evaluations. We tested the algorithm in Computer Go and measured an increase in playing strength of more than 100 ELO. The resulting program was able to deal with difficult test cases which are known to pose a problem for Monte-Carlo Tree Search.

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Section: Methodsmentioning

confidence: 99%

Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Graf

Platzner

2016

Theoretical Computer Science

Self Cite

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“…One can construct a prediction model capable of anticipating promising moves in a given situation of the game. Graf and Platzner (2014) introduce a prediction model for the game of Go. The model features patterns extracted from common fate graph -a graph representation of the board (Graepel et al, 2001).…”

Section: Games With Perfect Informationmentioning

confidence: 99%

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

Świechowski,

Godlewski,

Sawicki

et al. 2021

Preprint

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Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-ofthe-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problemdependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.

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“…Graphical representation methods have been applied to rock-paper-scissors games [17], dominance games [18], and strategy games [19]. In the game of Go, Graf and Platzner introduced a common fate graph (CFG) to represent the Go board and extracted features from it to predict moves with the Monte Carlo tree search method [20]. These methods effectively represent non-Euclidean games and enhance the relevance of game rules in strategy games.…”

Section: Introductionmentioning

confidence: 99%

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Xu,

Seng,

Ang

2023

Electronics

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Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides’ positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go.

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Common fate graph patterns in Monte Carlo Tree Search for computer go

Cited by 8 publications

References 13 publications

Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Adaptive playouts for online learning of policies during Monte Carlo Tree Search

Monte Carlo Tree Search: A Review of Recent Modifications and Applications

New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

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