Computer Othello Explained

Italic Title:no
Footnotes:NTest - a strong othello program

Computer Othello refers to computer architecture encompassing computer hardware and computer software capable of playing the game of Othello. It was notably included in Microsoft Windows from 1.0 to XP, where it is simply known as Reversi.

Availability

There are many Othello programs such as NTest, Saio, Edax, Cassio, Pointy Stone, Herakles, WZebra, and Logistello that can be downloaded from the Internet for free. These programs, when run on any up-to-date computer, can play games in which the best human players are easily defeated. This is because although the consequences of moves are predictable for both computers and humans, computers are better at exploring them.[1]

Search techniques

Computer Othello programs search for any possible legal moves using a game tree. In theory, they examine all positions / nodes, where each move by one player is called a "ply". This search continues until a certain maximum search depth or the program determines that a final "leaf" position has been reached.

A naive implementation of this approach, known as Minimax or Negamax, can only search to a small depth in a practical amount of time, so various methods have been devised to greatly increase the speed of the search for good moves. These are based on Alpha-beta pruning, Negascout, MTD(f), and NegaC*.[2] The alphabeta algorithm is a method for speeding up the Minimax searching routine by pruning off cases that will not be used anyway. This method takes advantage of the fact that every other level in the tree will maximize and every other level will minimize.[3]

Several heuristics are also used to reduce the size of the searched tree: good move ordering, transposition table and selective Search.[4]

To speed up the search on machines with multiple processors or cores, a "parallel search" may be implemented. Several experiments have been made with the game Othello, like ABDADA[5] or APHID[6] On recent programs, the YBWC[7] seems the preferred approach.

Multi-Prob cut

Multi-ProbCut is a heuristic used in alpha–beta pruning of the search tree.[8] The ProbCut heuristic estimates evaluation scores at deeper levels of the search tree using a linear regression between deeper and shallower scores. Multi-ProbCut extends this approach to multiple levels of the search tree. The linear regression itself is learned through previous tree searches, making the heuristic a kind of dynamic search control.[9] It is particularly useful in games such as Othello where there is a strong correlation between evaluations scores at deeper and shallower levels.[10] [11]

Evaluation techniques

There are three different paradigms for creating evaluation functions.

Disk-square tables

Different squares have different values - corners are good and the squares next to corners are bad. Disregarding symmetries, there are 10 different positions on a board, and each of these is given a value for each of the three possibilities: black disk, white disk and empty. A more sophisticated approach is to have different values for each position during the different stages of the game; e.g. corners are more important in the opening and early midgame than in the endgame.[12]

Mobility-based

Most human players strive to maximize mobility (number of moves available) and minimize frontier disks (disks adjacent to empty squares). Player mobility and opponent mobility are calculated, and player potential mobility and opponent potential mobility are calculated as well.[13] These measures can be found very quickly, and they significantly increase playing strength. Most programs have knowledge of edge and corner configurations and try to minimize the number of disks during the early midgame, another strategy used by human players.[12]

Pattern-based / pattern coefficients

Mobility maximization and frontier minimization can be broken down into local configurations which can be added together; the usual implementation is to evaluate each row, column, diagonal and corner configuration separately and add together the values, many different patterns have to be evaluated.[12] The process of determining values for all configurations is done by taking a large database of games played between strong players and calculating statistics for each configuration in each game stage from all the games.[12]

The most common choice to predict the final disc difference uses a weighted disk difference measure where the winning side gets a bonus corresponding to the number of disks.[12]

Opening book

Opening books aid computer programs by giving common openings that are considered good ways to counter poor openings. All strong programs use opening books and update their books automatically after each game. To go through all positions from all games in the game database and determine the best move not played in any database game, transposition tables are used to record positions that have been previously searched. This means those positions do not need to be searched again.[12] This is time-consuming as a deep search must be performed for each position, but once this is done, updating the book is easy. After each game played, all new positions are searched for the best deviation.

Other optimizations

Faster hardware and additional processors can improve Othello-playing program abilities, such as deeper ply searching.

Solving Othello

During gameplay, players alternate moves. The human player uses black counters while the computer uses white. The human player starts the game. Othello is strongly solved on 4×4 and 6×6 boards, with the second player (white) winning in perfect play.[14] [15]

Othello 4 × 4

Othello 4x4 has a very small game tree and has been solved in less than one second by many simple Othello programs that use the Minimax method, which generates all possible positions (nearly 10 million). The result is that white wins with a +8 margin (3-11).[14]

Othello 6 × 6

Othello 6x6 has been solved in less than 100 hours by many simple Othello programs that use the Minimax method, which generates all possible positions (nearly 3.6 trillion). The result is that white wins with a +4 margin (16-20).[16]

Othello 8 × 8

The Othello 8x8 game tree size is estimated at 1054 nodes, and the number of legal positions is estimated at less than 1028. As of October 2023, a preprint claims that the game has been solved, with optimal result being draw.[17] [18] Computation results is also shared, making it one of the largest publicly available book.[19]

Some top programs have expanded their books for many years now. As a result, many lines are in practice draws or wins for either side. Regarding the three main openings of diagonal, perpendicular and parallel, it appears that both diagonal and perpendicular openings lead to drawing lines, while the parallel opening is a win for black. The drawing tree also seems bigger after the diagonal opening than after the perpendicular opening.[20] The parallel opening has strong advantages for the black player, enabling black to always win in a perfect play.[21]

Milestones in computer Othello

List of top Othello/ Reversi programs

  1. NTest (Ntest) by Chris Welty
  2. Edax (Edax) by Richard Delorme
  3. Logistello (Logistello) by Michael Buro

See also

External links

Notes and References

  1. Web site: Dcs.gla.ac.uk. https://web.archive.org/web/20110103130600/http://www.dcs.gla.ac.uk/~daw/masters-projects/dissertations/Colquhoun.2008.pdf. dead. January 3, 2011.
  2. Jean-Christophe Weill (1992). The NegaC* Search. ICCA Journal, Vol. 15, No. 1, pp. 3-7.
  3. Armanto. Hendrawan. Santoso, Joan . Giovanni, Daniel . Kurniawan, Faris . Yudianto, Ricky . Gunawan, Steven . Evolutionary Neural Network for Othello Game. Procedia - Social and Behavioral Sciences. October 2012. 57. 419–425. 10.1016/j.sbspro.2012.09.1206. free.
  4. Buro, M., "Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello", Games in AI Research, H.J. van den Herik, H. Iida (ed.),, 2000
  5. Jean-Christophe Weill (1996). The ABDADA Distributed Minimax Search Algorithm. Proceedings of the 1996 ACM Computer Science Conference, pp. 131-138. ACM, New York, N.Y, reprinted ICCA Journal Vol. 19, No. 1
  6. Mark Brockington (1997). KEYANO Unplugged - The Construction of an Othello Program. Technical Report TR-97-05, Department of Computing Science, University of Alberta.
  7. Rainer Feldmann, Peter Mysliwietz, Burkhard Monien (1991). A Fully Distributed Chess Program. Advances in Computer Chess 6
  8. Buro. Michael. 1997. Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello. Games in AI Research. en. 34. 4. 77–96.
  9. Bulitko . Vadim . Lustrek . Mitja . Schaeffer . Jonathan . Bjornsson . Yngvi . Sigmundarson . Sverrir . Dynamic control in real-time heuristic search . Journal of Artificial Intelligence Research . 1 June 2008 . 32 . 419–452 . EN. 10.1613/jair.2497 . free .
  10. Book: Fürnkranz . Johannes . Machines that learn to play games Guide books . 2001 . Nova Science Publishers, Inc. . Nova Science Publishers, Inc.6080 Jericho Tpke. Suite 207 Commack, NYUnited States . 978-1-59033-021-0 . 11–59 .
  11. Book: Heinz . Ernst A. . Scalable Search in Computer Chess: Algorithmic Enhancements and Experiments at High Search Depths . 2013 . Springer Science & Business Media . 978-3-322-90178-1 . 32 . en.
  12. Web site: Writing an Othello program. 2023-02-12. radagast.se. Gunnar Andersson. 2007.
  13. http://othellogateway.com/ntest/Ntest/howitworks.htm How Ntest Works
  14. http://ailab.awardspace.com/othello4x4.html Solution of Othello 4 × 4
  15. http://www.feinst.demon.co.uk/Othello/6x6sol.html Perfect play in 6x6 Othello from two alternative starting positions
  16. Web site: Tothello home page. 2023-02-12. www.tothello.com. F. Pittner. July 2006.
  17. Web site: Othello is Solved . 2023-11-04.
  18. Web site: Takizawa . Hiroki . reversi-scripts . Github . 4 November 2023.
  19. Web site: Analyses of the Game of Othello's Positions . 2023-11-04.
  20. Web site: Strongest othello program in term of artificial intelligent . 2010-04-05 . https://web.archive.org/web/20070109170329/http://othellogateway.com/ntest . 2007-01-09 . dead .
  21. Web site: SaioApp project – The strongest Othello engine. 2023-02-12. en-US.
  22. Gardner, Martin. Mathematical Recreations. Scientific American, April 1977.
  23. Duda . Richard O . October 1977 . Othello, a New Ancient Game . BYTE . 60–62.
  24. News: Othello . Creative Computing . November–December 1977 . 18 October 2013 . Wright, Ed . 140–142.
  25. Web site: Computer Othello - Videogame by Nintendo.
  26. Web site: The History of Computer Games. https://web.archive.org/web/20110124212107/http://userhome.brooklyn.cuny.edu/cirasella/Presentations/computer_games_handout.pdf. dead. January 24, 2011.
  27. News: Simulating Human Decision-Making on a Personal Computer . BYTE . July 1980 . 18 October 2013 . Frey, Peter W . 56.
  28. News: The Santa Cruz Open / Othello Tournament for Computers . BYTE . July 1981 . 18 October 2013 . Frey, Peter W . 16.
  29. Web site: Othello match of the year. 2023-02-12. skatgame.net. Michael Buro. 20 August 1997.
  30. Web site: Yamana . Takuto . Egaroucid Self-Play Transcripts . Othello AI Egaroucid . 5 November 2023.

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