Factorization Ranking Model for Move Prediction in the Game of Go

Xiao, Chenjun; Müller, Martin

doi:10.1609/aaai.v30i1.10180

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…To model human play the method proposed by Xiao and Müller (2016) was used as a foundation. However, to reduce the number of parameters, we removed the pairwise interactions between features from their model ranking the relative strength of moves.…”

Section: Modelling Human Playmentioning

confidence: 99%

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play

Devlin

Anspoka

Sephton

et al. 2021

AIIDE

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Monte Carlo Tree Search (MCTS) has become a popular solution for controlling non-player characters. Its use has repeatedly been shown to be capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control non-player characters. In collaboration with the developers, we collected gameplay data from 27,592 games and showed in a previous study that the playstyle of human players significantly differed from that of the non-player characters. This paper presents a method of biasing MCTS using human gameplay data to create Spades playing agents that emulate human play whilst maintaining a strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are generally applicable to digital games with discrete actions.

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Section: Modelling Human Playmentioning

confidence: 99%

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play

Devlin

Anspoka

Sephton

et al. 2021

AIIDE

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“…Supervised pattern-matching policy learning. Go professionals rely heavily on pattern analysis rather than brute simulation in most cases (Clark and Storkey 2015;Xiao and Müller 2016). They can gain strong intuitions about what are the best moves to consider at a glance.…”

Section: Related Workmentioning

confidence: 99%

Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Wang

et al. 2017

AAAI

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Monte Carlo tree search (MCTS) is extremely popular in computer Go which determines each action by enormous simulations in a broad and deep search tree. However, human experts select most actions by pattern analysis and careful evaluation rather than brute search of millions of future interactions. In this paper, we propose a computer Go system that follows experts’ way of thinking and playing. Our system consists of two parts. The first part is a novel deep alternative neural network (DANN) used to generate candidates of next move. Compared with existing deep convolutional neural network (DCNN), DANN inserts recurrent layer after each convolutional layer and stacks them in an alternative manner. We show such setting can preserve more contexts of local features and its evolutions which are beneficial for move prediction. The second part is a long-term evaluation (LTE) module used to provide a reliable evaluation of candidates rather than a single probability from move predictor. This is consistent with human experts’ nature of playing since they can foresee tens of steps to give an accurate estimation of candidates. In our system, for each candidate, LTE calculates a cumulative reward after several future interactions when local variations are settled. Combining criteria from the two parts, our system determines the optimal choice of next move. For more comprehensive experiments, we introduce a new professional Go dataset (PGD), consisting of $253,233$ professional records. Experiments on GoGoD and PGD datasets show the DANN can substantially improve performance of move prediction over pure DCNN. When combining LTE, our system outperforms most relevant approaches and open engines based on MCTS.

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Analyzing the Impact of Knowledge and Search in Monte Carlo Tree Search in Go

Haqiqat

Müller

2019

Communications in Computer and Information Science

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Factorization Ranking Model for Move Prediction in the Game of Go

Cited by 5 publications

References 10 publications

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play

Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play

Beyond Monte Carlo Tree Search: Playing Go with Deep Alternative Neural Network and Long-Term Evaluation

Analyzing the Impact of Knowledge and Search in Monte Carlo Tree Search in Go

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