Othello

Othello
Sean Farrell
July 13, 2017
Othello
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Two-player game played on
8x8 board
All pieces have one white side
and one black side
Initial board setup is shown
right, with valid moves marked
as dots
The Evolution of Strong Othello Programs
Michael Buro, 2003
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First computer Othello tournaments in 1979
In 1980, first time a World-champion lost a
game of skill against a computer
6-0 defeat of then human World-champion in
1997
Evaluation Function Evolution
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Mini-max search used to estimate the chance
of winning for the player with current move
Construct function using features of board
state that correlate with winning
Important features
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Disc stability – stable discs cannot be flipped
Disc mobility – move options
Disc parity – last move opportunities for every
empty board region
IAGO (1982)
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Classic, hand-crafted evaluation function
Features were chosen based on analysis of
Othello
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Edge Stability & Internal Stability
Current Mobility & Potential Mobility
Evaluation parameters were done manually
BILL (1990)
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Partly pattern based, feature weights are
learned
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Edge Stability
Current Mobility & Potential Mobility
Sequence Penalty
Evaluation speed increased by using precomputed tables
In comparison, BILL wins all games against
IAGO, with 20% the thinking time
Logistello-1(1994)
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Pattern values learned independently from
sample data
Uses logistic regression to combine features
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Current Mobility & Potential Mobility
Patterns
Logistello-1(1994)
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Evaluation function is entirely table based
All evaluation parameters are learned from
sample positions
Logistello uses selective search heuristic
called ProbCut
Dominated computer Othello until 1996
Logistello-2(1997)
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Joint learning of pattern values
Assigning pattern values independently
neglected pattern correlations
Can assign arbitrary values to patterns
whose meaning is not bound by limited
human understanding of the problem
Strength increase from 1994 version is tenfold
Other Important Improvements
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Opening books
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Saves time
Avoids falling into known strategic traps
Less chance of losing two games in the same way
End game search
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Allows for optimal play near end
Takeshi Murakami vs. Logistello
Michael Buro, 1997
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Both humans and computers have improved
playing Othello
Against imperfect human players it pays off to
complicate endgame positions, not so with
strong Othello programs
Human vs. Computer
Takeshi Murakami
Michael Buro and is program Logistello
Discovering Complex Othello Strategies…
D. E. Moriarty & Risto Miikkulainen, 1997
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Found that experts search selective paths
through pattern recognition
Most programs used deeper searches to be
effective
Wanted a “human-like” approach
Neural Networks
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Networks learned Othello without previous
knowledge
No hand-coded rules or heuristics
Strategies evolved through play
No search mechanism
Goal was to discover strategies
Implementation
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Neural networks relied on pattern recognition
Used marker-based scheme with genetic
algorithms
Network architecture and weights evolve
Population of 50 networks
Initially evolved against random move maker
Results
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Positional strategy
against random mover
after 100 generations
Results
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Mobility strategy
against searcher after
2000 generations
Articles
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M. Buro, The Evolution of Strong Othello
Programs, 2003
M. Buro, Takeshi Murakami vs. Logistello,
1997
D. E. Moriarty & R. Miikkulainen, Discovering
Complex Othello Strategies Through
Evolutionary Neural Networks, 1995
Websites
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Michael Buro
http://www.cs.ualberta.ca/~mburo/
Neural Networks Research Group
http://nn.cs.utexas.edu/
Free online Othello game
http://brittany.angloinfo.com/games/iago.asp
Questions ??

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