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Computer Othello refers to computer architecture encompassing computer hardware and computer software capable of playing the game of Othello.
- 1 Availability
- 2 Search techniques
- 3 Evaluation techniques
- 4 Opening Book
- 5 Other optimizations
- 6 Solving Othello
- 7 Milestones in Computer Othello
- 8 List of Top Othello/ Reversi Programs
- 9 See also
- 10 Notes
- 11 External links
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 envisaging them.
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, NegaC*. 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.
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 or APHID On recent programs, the YBWC seems the preferred approach.
There are three different paradigms for creating evaluation functions.
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.
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. 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.
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, lots of different patterns have to be evaluated. 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.
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.
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. 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.
Faster hardware and additional processors can improve Othello-playing program abilities, such as deeper ply searching.
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. Although mathematically unsolved, it is practically solved on a standard 8×8 board as well. Computer analysis shows thousands of draw lines, or paths to a draw, although no such line has been fully proven.
Othello 4 x 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.
Othello 6 x 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.
Othello 8 x 8
The Othello 8x8 game tree size is estimated at 1054 nodes, and the number of legal positions is estimated at less than 1028. Although not mathematically solved yet, a solution could possibly be found using intensive computation with top programs on fast parallel hardware or through distributed computation.
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. The parallel opening has strong advantages for the black player, enabling him to always win in a perfect play. Although it is not proven yet, practically the game always ends in a draw if both players play perfectly. On standard games, using their opening book, top programs lose less than 1% of the time.
Othello 10 x 10
In a 10x10 Othello board, the starting player (black) is more likely to win. 10x10 has a longer mid-game. Computer analysis shows that a draw is highly likely if both players play perfectly. The game tree complexity is very high, estimated to be 1090, with the number of legal positions estimated at 1044.
Milestones in Computer Othello
1980: The Othello program Moor (written by Mike Reeve and David Levy) won one game in a six-game match against world champion Hiroshi Inoue. Peter W Frey of Northwestern University discussed computer and human Othello strategies in BYTE, and discussed his TRS-80 Othello game which, Frey claimed, easily defeated Wright's version running on a CDC 6600. Paul Rosenbloom of Carnegie Mellon University developed IAGO, which finished in third place at a Northwestern University computer tournament. When IAGO played The Moor, IAGO was better at capturing pieces permanently and limiting its opponent’s mobility.
1981: IAGO running on a DEC KA10 finished ahead of 19 other contestants at the Santa Cruz Open Othello Tournament at the University of California, Santa Cruz, with the only undefeated record. Charles Heath's TRS 80-based game finished in second place. Microcomputer CPU-based engines won the second through seventh places, ahead of several mainframes and minicomputers; Frey speculated that this was because computer Othello does not benefit from several of the advantages of larger computers, such as faster floating-point arithmetic.
Late 1980s: Kai-Fu Lee and Sanjoy Mahajan created the Othello program BILL, which was similar to IAGO but incorporated Bayesian learning. BILL reliably beat IAGO.
1992: Michael Buro began work on the Othello program Logistello. Logistello’s search techniques, evaluation function, and knowledge base of patterns were better than those in earlier programs. Logistello perfected its game by playing over 100,000 games against itself.
1997: Logistello won every game in a six-game match against world champion Takeshi Murakami. Though there had not been much doubt that Othello programs were stronger than humans, it had been 17 years since the last match between a computer and a reigning world champion. After the 1997 match, there was no longer any doubt: Logistello was significantly better than any human player.
1998: Michael Buro retired Logistello. Research interest in Othello waned somewhat, but some programs, including Ntest, Saio, Edax, Cassio, WZebra and Herakles, continued to be developed.
2004: Ntest became the strongest program, significantly stronger than Logistello.
2005: Ntest, Saio, Edax, Cyrano and WZebra, became significantly stronger than Logistello. Ntest and WZebra retired.
2011: Saio, Edax and Cyrano, became much faster in term of speed than Logistello and other programs.
List of Top Othello/ Reversi Programs
- Saio (Saio) by Benedetto Romano
- Cyrano by Bruno Causse
- Edax (Edax) by Richard Delorme
- Cassio (Cassio) by Stéphane Nicolet
- Ymatioun by Youri Matiounine
- Pirate by Roger H. Hughston
- NTest (Ntest) by Chris Welty
- WZebra (WZebra) by Gunnar Andersson
- Logistello by Michael Buro
- Pointy Stone (Pointy Stone) by Jonathan Kreuzer
- Herakles (Herakles) by Kostas Tournavitis - strongest othello 10x10 program
- Tothello (Tothello) by F. Pittner - strongest othello 4x4 and 6x6 program
- Iagno (Iagno) GNOME-Version of Reversi (Open-Source)
- Daisy Reversi (Daisy Reversi) by Pavel Matlash - strongest othello 12x12, 14x14, 16x16, 18x18, 20x20, 22x22 and 24x24 program
- Cyrano (Cyrano java applet) by Bruno Causse
- Jean-Christophe Weill (1992). The NegaC* Search. ICCA Journal, Vol. 15, No. 1, pp. 3-7.
- Armanto, Hendrawan; Santoso, Joan; Giovanni, Daniel; Kurniawan, Faris; Yudianto, Ricky; Gunawan, Steven (October 2012). "Evolutionary Neural Network for Othello Game". Procedia - Social and Behavioral Sciences 57: 419–425. Retrieved 2/10/13.
- 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.), ISBN 90-621-6416-1, 2000
- 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
- Mark Brockington (1997). KEYANO Unplugged - The Construction of an Othello Program. Technical Report TR-97-05, Department of Computing Science, University of Alberta.
- Rainer Feldmann, Peter Mysliwietz, Burkhard Monien (1991). A Fully Distributed Chess Program. Advances in Computer Chess 6
- Writing an Othello program April 02, 2007
- How Ntest Works March 02, 2005
- Solution of Othello 4 x 4 September 02, 2008
- Perfect play in 6x6 Othello from two alternative starting positions November 17, 2004
- Edax 4.0 Opening Book November 01, 2008
- A free software for solving 4x4 and 6x6 othello
- Strongest othello program in term of artificial intelligent
- Saio's book
- Wright, Ed (November-December 1977). "Othello". Creative Computing. pp. 140–142. Retrieved 18 October 2013.
- Frey, Peter W (July 1980). "Simulating Human Decision-Making on a Personal Computer". BYTE. p. 56. Retrieved 18 October 2013.
- The History of Computer Games
- Frey, Peter W (July 1981). "The Santa Cruz Open / Othello Tournament for Computers". BYTE. p. 16. Retrieved 18 October 2013.
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