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Computer Chess 2004 . How far have we come? By Mike Donner And Dan Mathia. Goals:. To see how computer chess has evolved throughout the years To show some of the theory behind the Computer Chess Masters

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computer chess 2004

Computer Chess 2004

How far have we come?

By Mike Donner

And

Dan Mathia

goals
Goals:
  • To see how computer chess has evolved throughout the years
  • To show some of the theory behind the Computer Chess Masters
  • To demonstrate the results of different chess programs squaring off for dominance of our Artificial Chess world
slide3

History

1957 - Newell and Simon predicted that a computer would be chess champion within ten years

Simon : “I was a little far-sighted with chess, but there was no way to do it with machines that were as slow as the ones way back then”

1958 - First computer to play chess was an IBM 704 - about one millionth capacity of deep blue.

1967 : Mac Hack competed successfully in human tournaments

1983 : “Belle” obtained expert status from the United States Chess Federation

Mid 80’s : Scientists at Carnegie Mellon University started work on what was to become Deep Blue.

Project moved to IBM in 1989

slide4

Computer Chess Masters

  • Players we did not have access but will discuss to:
    • Deep Thought 1987
    • Deep Blue 1997
    • Hal (2001 Space Odyssey)
  • Others between 1950’s to1980’s
    • Mac Hack 3
    • Chess 3
    • Chess 4
    • Belle
slide5

Deep Thought

  • Built at Carnegie-Mellon University in the 1980s
  • Example Deep Thought Code:

#define min(x,y) ((x) < (y) ? (x) : (y))

#define max(x,y) ((x) > (y) ? (x) : (y))

  • How Deep Thought “thought”
    • Used different table for evaluation function
      • Piece placement table
        • Wieghts were different for each side.
          • Stong black move = weak white move
      • Piece value table
        • Queen = 9
        • Rook = 5
        • Knight = 3
        • Pawn = 1
slide6

Deep Blue

  • First Created in 1997 (Deep thought All grown up)
  • algorithm:
    • iterative-deepening alpha-beta search, transposition table, databases including openings of grandmaster games (700,000), and endgames (all with 5 pieces or more pieces remaining)
  • hardware:
    • 30 IBM RS/6000 processors
      • They do: high level decision making
    • 480 custom chess processors
      • all running in parallel
      • They do :
        • deep searches into the trees
        • move generation and ordering,
        • position evaluation (over 8000 evaluation points)
  • average performance:
    • 126 million nodes/sec., 30 billion position/move generated, search depth: 14
slide7

Hal

  • Hal showed a human quality when playing chess.
    • Hal played “trappy” chess
      • Meaning the obvious move for the opponent is a one of loss.
  • Hal chose a slower strategy than current computers.
    • Showing a more “human” side
slide8

Players Ratings

  • How will our players match up
  • Deep Thought 1987
  • Deep Blue 1997
  • Mac Hack 3
  • Chess 3
  • Chess 4
  • Belle
slide9

Algorthims: Trappy vs Minimax

  • The term “Trappy” comes from the strategy of trying to trap your opponent into making a bad move.
    • This algorithm needs creativity
  • Minimax goes on the best possible move, assuming the opponent has chosen the best move.
    • This algorithm goes the fast ‘mate
slide10

Search Trees

  • Games can be viewed as as search problems, then you can model it into a tree.
  • The basic problem with looking at games as search trees is that these trees can get very large.
slide11

Strategy: The Minimax Algorithm

  • MiniMax Rules state optimal position as combination of best position for player and worst position for opponent
  • Heuristic Evaluation Function assigns numerical value to each searched position
  • Using Alpha-Beta Pruning with the heuristic values allows for:
  • eliminates repetitious evaluations
  • provides a valid refutation for bad positions
  • provides search tree in best-first order

heuristic evaluation + minimax rules => minimaxed heuristic values

slide12

Computer Chess Masters

Players That Squared off to be come our champions of this

Artificial Chess Universe:

Chessmaster 8,000

Star Wars Chess

Chessterfield vi3

slide13

The process by which we locked these Chess Masters in battle for supremacy in our

Artificial Chess Universe

  • Computer A
    • CPU = White
    • Human = Black
  • Computer B
    • CPU = Black
    • Human = White
  • Move:
    • CPU A = Human B
    • Human A = CPU B
slide14

Chessterfield vi3

Representing our early Chess games

  • Player Facts:
  • Product built by M. Luscher in his spare time
  • Produced in 1999
  • The .exe file’s size is 640KB and has no support files
slide15

Star Wars Chess

  • Very Entertaining with the elements of your favorite Star Wars characters
  • Over 72 different capture sequences
  • Identifies many common defense as they are played
  • Player Facts:
  • Product made for production built by Software Toolworks
  • Produced in 1994
  • Has the ability to play at various levels
  • Beginner to Expert
slide16

Chessmaster 8,000

  • Player Facts:
  • Product made for production built by Ubisoft
  • Produced in 2001
  • Has the ability to play with the tendencies of both current and past players
  • Can be configured to play at any level and will rate you as a player
  • LOTS of bells and Whistles (you get what you pay for)
slide18

Results of Trials

  • Despite the production years Star Wars (1994) chess had a more efficient search algorithm. It proved this by beating the Chessterfield (1999) 3 matches in a row.
  • However Star Wars could not beat ChessMaster 8,000. Due less advanced pruning techniques
slide19

More Results

  • Giving the different programs varying search times showed how the program searched the tree
    • Given 30 sec. Chessterfield took between 5-30sec and made its move as soon as it found one--considered safe or if the time was up just made it’s safest known move
    • Given 30 sec. Star Wars would take the whole 30 sec. to make a move.
  • Therefore we can see the Chessterfield used minimax moving when the best move was found. Star Wars used breadth first search to look through the entire level of the tree.
this proves
This Proves:
  • Computers became more advanced
      • Better search algorithms
      • Better chess techniques
      • Therefore better chess players from computers.
slide21

Conclusion

  • Computers have come a long way since the 1950s. From MacHack 3 to Deep Blue
  • Theories and strategies behind computer chess have become more developed.
    • The more efficient searches have proven advanced enough to outsmart some of the world’s greatest master.
  • This was seen as the older verison (Chessterfield vi3) could not beat newer version (Star Wars) which could not beat the current grand master (Chessmaster 8,000.)
slide22

Why Computers Chess will not play on the same plane as humans

  • They are not able use psychology
  • Psychology, as any top chess player will tell you, is an important key to winning chess. But Deep Blue has no psychological perception, can neither intimidate nor be intimidated, and experiences no joy from winning nor sadness from losing.
    • However Deep Blue will use timing functions
      • Given a total of 3.5 hours to make all its moves, it can ration time in a variety of ways. It can average the number of moves and attempt to deviate from that only by a small margin. Or it can move very fast, forcing Kasparov to respond. Or it can take an inordinate amount of time over one move, calculating many trillions of possible games, forcing Kasparov to wait and possibly become bored or agitated.

According to :

Research.IBM.com

slide23

Future of Computer Chess

  • The only lacking element to make the perfect computer chess player is the ability to reason:
    • That means even now with the computer’s computation power, the computer cannot adapt to weaknesses in its opponents. The computer only knows the possible steps or the code can be manipulated to for the desired outcome (simulating “trappy” strategies)
    • Once the program can recognize player’s game tendencies then it should adjust its game tree and heuristic value accordingly. Making an adaptable program.
  • Deep Blue can force a win or a draw no matter what. Once the board is reduced down to 6 pieces in as little as 262 Moves.
    • However chess tournaments only allows for 50 moves. So Deep Blue making a brute force search with his algorithms and search trees won’t do him much good in tournament style play.
the end
THE END

OR

  • How About another Game DAVE?
references
References
  • Hal’s Legacy published by MIT Press Cambridge, Massachusetts 1997
    • Info About Hal’s play and history
  • Reference.IBM.com
    • Information about Deep Blue
  • ChessBase.com
    • Basic chess info and intresting facts
  • www.informatik.uni-stuttgart.de/ipvr/ise/lehre/ProblemSolving/
    • Website about search methods
  • www.tim-mann.org/chess.htm
    • Info about deep thought
  • www.chessmasternetwork.com
    • Info about ChessMaster 8,000
  • home.datacomm.ch/m.luescher
    • Home of Chessterfield