1991 — 90 p.

На англ. языке.The game of go is an ideal problem domain for exploring machine learning: it is easy to define and there are many human experts, yet existing programs have failed to emulate their level of play to date. Existing literature on go playing programs and applications of machine learning to games are surveyed. An error function based on a database of master games is defined which is used to formulate the learning of go as an optimization problem. A classification technique called pattern preference is presented which is able to automatically derive patterns representative of good moves; a hashing technique allows pattern preference to run efficiently on conventional hardware with graceful degradation as memory size decreases.

1991 - 90 p.

棋类游戏是探索机器学习的理想问题领域。围棋游戏是探索机器学习的理想问题领域：它很容易定义，而且有许多人类专家，但现有的程序至今未能模仿他们的游戏水平。对现有的围棋程序的文献和机器学习在游戏中的应用进行了调查。定义了一个基于主棋谱数据库的误差函数，该函数被用来将围棋的学习制定为一个优化问题。提出了一种被称为模式偏好的分类技术，它能够自动得出代表好棋的模式；散列技术使模式偏好能够在传统硬件上有效运行，并随着内存大小的减少而优雅地退化。