Evolving Neural Networks to Play Go y

نویسندگان

  • Norman Richards
  • David E. Moriarty
  • Risto Miikkulainen
چکیده

Go is a di cult game for computers to master, and the best go programs are still weaker than the average human player. Since the traditional game playing techniques have proven inadequate, new approaches to computer go need to be studied. This paper presents a new approach to learning to play go. The SANE (Symbiotic, Adaptive Neuro-Evolution) method was used to evolve networks capable of playing go on small boards with no pre-programmed go knowledge. On a 9 9 go board, networks that were able to defeat a simple computer opponent were evolved within a few hundred generations. Most signi cantly, the networks exhibited several aspects of general go playing, which suggests the approach could scale up well.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Evolving Go Playing Strategy in Neural Networks

The game of Go is an ideal problem domain for exploring machine learning: it has simple rules yet requires more and more complex strategies to play well as the board size is increased. Despite much effort, existing Go programs, which have largely employed knowledge based and symbolic AI techniques, have failed to achieve a standard much above an average human amateur. This paper examines the po...

متن کامل

Teaching Deep Convolutional Neural Networks to Play Go

Mastering the game of Go has remained a long standing challenge to the field of AI. Modern computer Go systems rely on processing millions of possible future positions to play well, but intuitively a stronger and more ‘humanlike’ way to play the game would be to rely on pattern recognition abilities rather then brute force computation. Following this sentiment, we train deep convolutional neura...

متن کامل

Training Deep Convolutional Neural Networks to Play Go

Mastering the game of Go has remained a longstanding challenge to the field of AI. Modern computer Go programs rely on processing millions of possible future positions to play well, but intuitively a stronger and more ‘humanlike’ way to play the game would be to rely on pattern recognition rather than brute force computation. Following this sentiment, we train deep convolutional neural networks...

متن کامل

Evolving neural networks to play checkers without relying on expert knowledge

An experiment was conducted where neural networks compete for survival in an evolving population based on their ability to play checkers. More specifically, multilayer feedforward neural networks were used to evaluate alternative board positions and games were played using a minimax search strategy. At each generation, the extant neural networks were paired in competitions and selection was use...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 1997