Earticle

초록

영어

Unlike other games such as chess, draughts and backgammon, computers are currently quite weak at the game of go ( baduk). Brute force is difficult due to the higher branching factor and game length. Human made algorithms become very complex before even approaching human strength on a subproblem of the game. One possible approach to this challenging problem is to use machine learning to let the program learn and improve without increased human effort. Machine learning has been successful in other games (e.g. draughts, backgammon). In this paper we give an overview of existing techniques. We discuss different aspects of learning, and propose some directions of research. In particular we believe that a first order representation language combined with a multistrategy learning system can achieve much more than what currently exists.

목차

1. Introduction
 2. Computer game playing
  2.1 Min-max search and Alpha-beta pruning
  2.2 Endgame theory
 3. What can be learned?
  3.1 Global approaches
  3.2 Learning in search
  3.3 Learning in the endgame
  3.4 Learning in the opening
  3.5 Higher level concepts
 4. The representation language
 5. Experiments
  5.1 Learning a heuristic for Tsume-Go problems
  5.2 Learning an opening book with temperatures
 6. Conclusions and further work
 Acknowledements
 References

키워드

저자

• Jan Ramon [ Department of Computer Science, Katholieke Universiteit Leuven Celestijnenlaan 200A, B-3001 Leuven, Belgium ]
• Hendrik Blockeel [ Department of Computer Science, Katholieke Universiteit Leuven Celestijnenlaan 200A, B-3001 Leuven, Belgium ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국바둑학회 국제학술대회논문집 [國際學術大會論文集]

• 간기

  연간

• 수록기간

  2001~2006

• 십진분류

  KDC 691 DDC 794