The rectangular seeds of Domineering, Atari-Go and Breakthrough

نویسندگان

  • Tristan Cazenave
  • Jialin Liu
  • Olivier Teytaud
چکیده

Recently, a methodology has been proposed for boosting the computational intelligence of randomized gameplaying programs. We modify this methodology by working on rectangular, rather than square, matrices; and we apply it to the Domineering game. At CIG 2015, We propose a demo in the case of Go. Hence, players on site can contribute to the scientific validation by playing (in a double blind manner) against both the original algorithm and its boosted version.

برای دانلود رایگان 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

Playout Policy Adaptation for Games

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). We propose to learn a playout policy online so as to improve MCTS for GGP. We test the resulting algorithm named Playout Policy Adaptation (PPA) on Atarigo, Breakthrough, Misere Breakthrough, Domineering, Misere Domineering, Go, Knightthrough, Misere Knightthrough, Nogo and Misere Nogo. For most of ...

متن کامل

An update on domineering on rectangular boards

Domineering is a combinatorial game played on a subset of a rectangular grid between two players. Each board position can be put into one of four outcome classes based on who the winner will be if both players play optimally. In this note, we review previous work, establish the outcome classes for several dimensions of rectangular board, and restrict the outcome class in several more.

متن کامل

Who Wins Domineering on Rectangular Boards?

Using mostly elementary considerations, we find out who wins the game of Domineering on all rectangular boards of width 2, 3, 5, and 7. We obtain bounds on other boards as well, and prove the existence of polynomial-time strategies for playing on all boards of width 2, 3, 4, 5, 7, 9, and 11. We also comment briefly on toroidal and cylindrical boards.

متن کامل

Playout policy adaptation with move features

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). We propose to learn a playout policy online so as to improve MCTS for GGP. We also propose to learn a policy not only using the moves but also according to the features of the moves. We test the resulting algorithms named Playout Policy Adaptation (PPA) and Playout Policy Adaptation with move Featur...

متن کامل

Memorizing the Playout Policy

Monte Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP). Playout Policy Adaptation with move Features (PPAF) is a state of the art MCTS algorithm that learns a playout policy online. We propose a simple modification to PPAF consisting in memorizing the learned policy from one move to the next. We test PPAF with memorization (PPAFM) against PPAF and UCT fo...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015