The quality, quantity, and consistency of the knowledge used in GO-playing programs often determine their strengths, and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial fo...The quality, quantity, and consistency of the knowledge used in GO-playing programs often determine their strengths, and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial for successful GO playing. In a previous article of this subject, we have presented an algorithm for efficient and automatic acquisition of spatial patterns of GO as well as their frequency of occurrence from game records. In this article, we present two algorithms, one for efficient and automatic acquisition of pairs of spatial patterns that appear jointly in a local context, and the other for deter- mining whether the joint pattern appearances are of certain significance statistically and not just a coincidence. Results of the two algorithms include 1 779 966 pairs of spatial patterns acquired automatically from 16 067 game records of professsional GO players, of which about 99.8% are qualified as pattern collocations with a statistical confidence of 99.5% or higher.展开更多
Computer programs of GO are typically constructed using a knowledge-based approach with heuristics and pattern matching because of enormous complexities of the game. In this approach, quantity, quality, and consistenc...Computer programs of GO are typically constructed using a knowledge-based approach with heuristics and pattern matching because of enormous complexities of the game. In this approach, quantity, quality, and consistency of patterns used in computer programs of GO to a large extent determine the strengths of the programs. This study presents an effective method to acquire automatically comprehensive GO patterns from large collections of game records. Statistical usages of the patterns ensure consistency and quality of the patterns, which in turn can help improve the strengths of computer GO programs. Additionally, statistical usages of patterns from different sources of game records clearly show subtle and significant discrepancies among various types of GO players, and clarify certain myths in the playing of GO.展开更多
文摘The quality, quantity, and consistency of the knowledge used in GO-playing programs often determine their strengths, and automatic acquisition of large amounts of high-quality and consistent GO knowledge is crucial for successful GO playing. In a previous article of this subject, we have presented an algorithm for efficient and automatic acquisition of spatial patterns of GO as well as their frequency of occurrence from game records. In this article, we present two algorithms, one for efficient and automatic acquisition of pairs of spatial patterns that appear jointly in a local context, and the other for deter- mining whether the joint pattern appearances are of certain significance statistically and not just a coincidence. Results of the two algorithms include 1 779 966 pairs of spatial patterns acquired automatically from 16 067 game records of professsional GO players, of which about 99.8% are qualified as pattern collocations with a statistical confidence of 99.5% or higher.
文摘Computer programs of GO are typically constructed using a knowledge-based approach with heuristics and pattern matching because of enormous complexities of the game. In this approach, quantity, quality, and consistency of patterns used in computer programs of GO to a large extent determine the strengths of the programs. This study presents an effective method to acquire automatically comprehensive GO patterns from large collections of game records. Statistical usages of the patterns ensure consistency and quality of the patterns, which in turn can help improve the strengths of computer GO programs. Additionally, statistical usages of patterns from different sources of game records clearly show subtle and significant discrepancies among various types of GO players, and clarify certain myths in the playing of GO.
