INTRODUCING A ROUND ROBIN

TOURNAMENT INTO BLONDIE24

Belal Al-Khateeb Graham Kendall bxk@cs.nott.ac.uk gxk@cs.nott.ac.uk

School of Computer Science (ASAP Group)

University of Nottingham

Outline

-Introduction

- Checkers

- Samuel’s Checkers Program

- Chinook

- Deep Blue

- Blondie24

- Blondie24-R

- Blondie24-RR

- Results and Discussion

- Conclusion

- Future Work

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Checkers3

Opening Board of Checkers (Black moves first)

Checkers4

Black Forced to make Jump

move

Checkers5

Black Gets King

Samuel’s Checkers Program

- 1959, Arthur Samuel started to look at

Checkers

- The determination of weights through

self-play

- 39 Features

- Included look-ahead via mini-max (Alpha-

Beta)

- Defeated Robert Nealy

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Chinook

- Produced by Jonathan Schaeffer in 1989.

- 40,000 openings.

- 8-piece endgame database in 1994.

- Won the 1989 Computer Olympiad.

- Chinook become the world champion. The

first automated game player to have

achieved this.

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Deep Blue

- Developed by IBM in mid 1990s.

- An attempt to create a Chess program that

was capable of beating the world champion

at that time

- 30 processors with parallel search, could

evaluate up to 200 million chess positions

per second

- 8,000 different features

- The opening database in Deep Blue

consisted of 4,000 positions

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Deep Blue

- The end game database of Deep Blue

consists of all positions with five or fewer

chess pieces on the board.

- Defeated Gary Kasparov in a six-game

match in 1997 to become the first computer

program to defeat a world Chess champion.

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Blondie24

- Produced by Fogel in 1999-2000

- Neural network as an evaluation function.

- Values for input nodes

Red (Black) – positive

White – negative

Empty – zero

- Piece differential

- Subsections (sub-boards)

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Blondie2411

Blondie24’s EANN Architecture

Blondie24

- Initial population of 30 neural networks

(players).

- Each neural network plays 5 games (as red)

against 5 randomly chosen players:-

+1 for a win

0 for a draw

-2 for a loss

-Best 15 players retained, the other 15 players

eliminated.

-Copy the best 15 players (replacing the worst

15) and mutate the weights.

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Blondie24

- Repeat the process for 840 generations and

the best player after these generations is

retained.

- Played 165 games at zone.com.

- Rating: 2045.85 at that time

- In top 500 of over 120,000 players on

zone.com at that time.

- Better than 99.61% of registered players on

zone.com

End Product

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Blondie2414

Blondie24 Performance after 165 games on

zone.com

Blondie24-R

- Has the same structure and architecture that

Fogel utilised in Blondie24.

- The only exception that the value of the King

is fixed to 2.

- The King is more valuable than an ordinary

piece, and this is a well-known, even to

novice players.

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Blondie24-RR

- Eliminate the randomness in the evolutionary

phase of Blondie24-R.

- A league competition between all the 30

neural networks.

- All the neural networks play against each

other.

- The total number of matches per generation

will be 870 (30*29) rather than 150 (30*5).

- This increase (number of matches) will

decrease the number of generations (840

verses 145).

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Results and Discussion17

Blondie24-R Blondie24-RR Online WinCheck3D SX checkers

Blondie24-R - Draw Win Lose (7-Piece) Lose (8-Piece)

Blondie24-RR Win - Win Lose (2-Pieces ) Lose (4-Pieces)

Results of Playing Against Selected Programs

Results and Discussion

- Blondie24-RR plays two matches (one as red

and one as white) against Blondie24-

R, Blondie24-RR.

- Wins as red against Blondie24-R.

- The result is draw when Blondie24-RR moves

second.

- Reflects a success for our hypothesis based

on the fact that both players are end

products.

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Results and Discussion

- Blondie24-R and Blondie24-RR win against an

online program which can be considered as

another success.

- Plays against two programs (strong).

- For the first one Blondie24-RR lost with a two

piece difference, Blondie24-R lost with a seven

piece difference.

- Playing against the second program shows

that Blondie24-RR lost with a four piece

difference, while Blondie24-R lost with an eight

piece difference.

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Conclusion

- The results show that Blondie24-RR is

performing better than Blondie24-R.

- Based on these results it would seem

appropriate to use the league structure,

instead of only choosing five random

opponents to play against during the

evolutionary phase.

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Future Works

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- Investigate if other changes are possible.

- Investigate using individual and social

learning methods in order to enhance the

ability of Blondie24-RR to overcome the

problem of being an end product.

References

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1- Samuel, A. L., Some studies in machine learning using the game of checkers 1959,1967.

2- Fogel D. B., Blondie24 Playing at the Edge of AI, United States of America Academic Press, 2002.

3- Chellapilla K. and Fogel, D. B., Anaconda defeats hoyle 6-0: A case study competing an evolved

checkers program against commercially available software 2000.

4- Fogel D. B. and Chellapilla K., Verifying anaconda's expert rating by competing against Chinook:

experiments in co-evolving a neural checkers player.

5- Chellapilla K. and Fogel D.B., Evolution, Neural Networks, Games, and Intelligence,” 1999..

6- Chellapilla K. and Fogel D. B., Evolving an expert checkers playing program without using human

expertise.

7- Chellapilla K. and Fogel D. B., Evolving neural networks to play checkers without relying on

expert knowledge.1999.

Questions/Discussions

Thank You

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