• Corpus ID: 26183

Bayesian Generation and Integration of K-nearest-neighbor Patterns for 19x19 Go

@inproceedings{Bouzy2005BayesianGA,
  title={Bayesian Generation and Integration of K-nearest-neighbor Patterns for 19x19 Go},
  author={Bruno Bouzy and Guillaume Chaslot},
  booktitle={CIG},
  year={2005}
}
This paper describes the generation and utilisation of a pattern database for 19x19 go with the Knearest-neighbor representation. Patterns are generated by browsing recorded games of professional players. Meanwhile, their matching and playing probabilities are estimated. The database created is then integrated into an existing go program, INDIGO, either as an opening book or as an enrichment of other pre-existing hand-crafted databases used by INDIGO move generator. The improvement brought… 

Figures and Tables from this paper

A Fast Indexing Method for Monte-Carlo Go
TLDR
This paper proposes an effective method to learn the pattern weights from forty thousand professional games and shows that the method converges faster and performs equally well or better than the method of computing "Elo ratings" [4].
DOU Qing Automatic acquisition of pattern collocations in GO
TLDR
Two algorithms are presented, one for efficient and automatic acquisition of pairs of spatial patterns that appear jointly in a local context, and the other for determining whether the joint pattern appearances are of certain significance statistically and not just a coincidence.
Learning Patterns in the Game of Go MSc Thesis ( Afstudeerscriptie )
TLDR
This thesis focuses on learning methods for evaluating the quality of Go stones in a position by learning expert human positional classifiers using low level features such as the Relative Subgraph Features from the Common Fate Graph.
Computing "Elo Ratings" of Move Patterns in the Game of Go
  • Rémi Coulom
  • Computer Science
    J. Int. Comput. Games Assoc.
  • 2007
TLDR
A new Bayesian technique for supervised learning of move patterns from game records, based on a generalization of Elo ratings, which outperforms most previous pattern-learning algorithms, both in terms of mean log-evidence, and prediction rate.
Bayesian pattern ranking for move prediction in the game of Go
TLDR
A probability distribution over legal moves for professional play in a given position in Go is obtained and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34% of test positions.
Move Prediction in Go with the Maximum Entropy Method
TLDR
This work uses the relative frequencies of local board patterns observed in game records to generate a ranked list of moves, and then applies the maximum entropy method (MEM) to the list to re-rank the moves.
Contextual patterns and pattern collocations in the game of GO
TLDR
A systematic approach for knowledge representations of GO-playing in which the meaning of a move is defined as a contextual pattern with respect to local contexts of surroundings of the move, which allows large amounts of contextual patterns, along with their usage statistics, to be acquired efficiently from game records.
Using Patterns in Computer Go
TLDR
A technique for retrieving patterns from a collection of records of games played between human expert players, storing patterns, and implementing patterns in a computer program for Go is presented.
Liu Zhi-qing, Dou Qing
TLDR
This paper presents an effective method to automatically acquire comprehensive GO patterns from large collections of game records and ensures consistency and quality of the patterns, which, in turn, can help improve the strengths of computer GO programs.
...
...

References

SHOWING 1-10 OF 27 REFERENCES
Local Move Prediction in Go
TLDR
A system that learns to predict local strong expert moves in the game of Go at a level comparable to that of strong human kyu players is presented and experiments suggest that local move prediction will be a significant factor in enhancing the strength of Go programs.
GENERATION OF PATTERNS WITH EXTERNAL CONDITIONS FOR THE GAME OF GO
TLDR
This work has generated pattern databases for the game of Go that are associated to conditions that are external to the pattern, and believes that patterns associated to external conditions can be useful in other games.
Learning to score final positions in the game of Go
Neural Networks and Pattern Recognition
TLDR
Neural Networks and Pattern Recognition focuses on the use of neural networks in pattern recognition, a very important application area for neural networks technology.
Computer Go: An AI oriented survey
Learning to predict life and death from Go game records
Mathematical Morphology Applied to Computer Go
  • B. Bouzy
  • Computer Science
    Int. J. Pattern Recognit. Artif. Intell.
  • 2003
TLDR
A model is presented, derived from the closing operator of mathematical morphology and from the Zobrist's model, which yields very good results for "territory" recognition and efficient implementations of the dilation operator and territory recognition for computer go.
Games, computers, and artificial intelligence
Reinforcement Learning: An Introduction
TLDR
This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
...
...