Computer chess programs have evolved significantly since the first attempts in the 1940s and 1950s. Early programs evaluated positions based on material alone and searched game trees to a fixed depth. Modern programs use a variety of techniques to search more efficiently, including iterative deepening, transposition tables, null-move pruning, and dynamic move ordering based on histories and killer heuristics. Evaluation functions also consider pawn structure, king safety, and piece placement. The first program to defeat a human world champion was Deep Blue in 1997. State-of-the-art programs like Stockfish integrate extensive opening books, endgame databases, and advanced evaluation and search algorithms.

1. o History
o Implementation

2. 1) Claude E. Shannon (1949) Programming a Computer for
Playing Chess. In: Philosophical Magazine, Ser.7, Vol. 41, No. 314 -
March 1950.
2) T.A. Marsland (1992) Computer Chess and Search.
Encyclopedia of Artificial Intelligence, S.C. Shapiro (ed), pp. 224r-
241, Wiley & Sons, 2nd ed. 1992, ISBN 0471-50305-3.
3) Ernst A. Heinz (1997) How DarkThought Plays Chess, ICC A
Journal 20(3), pp. 166-176, Sept. 1997.
4) John L. Jerz (2009) A Proposed Heuristic for a Computer Chess
Program
5) Computer History Museum, Mastering the Game – A History
of Computer Chess, http://www.computerhistory.org/chess/
6) Wikipedia, accessed November 2009

3. Part 1:
History

4. In 1769 the Hungarian engineer
Baron Wolfgang von Kempelen
built a chess playing machine for
the amusement of the Austrian
Queen Maria Theresia. It was a
purely mechanical device, shaped
like a Turk.
Naturally its outstanding playing
strength was supplied by a chess
master cleverly hidden inside the
device. The machine was a fake.

6. In 1914 Torres y Quevedo
constructed a device which
played an end game of king and
rook against king
(Vigneron, 1914). The machine
played the side with king and
rook and would force checkmate
in few moves however its human
opponent played.
Since an explicit set of rules can be
given for making satisfactory
moves in such an end game, the
problem is relatively simple, but
the idea was quite advanced for
that period.

7. Gonzales Torres y Quevedo demonstrating the chess automaton to Norbert Wiener in 1951

8. ● In 1947, Turing
designed the first
program to play
chess and tested it
with paper and
pencil using
himself as the
computer

9. “Although perhaps of no
practical importance, the
question is of theoretical
interest, and it is hoped that
a satisfactory solution of
this problem will act as a
wedge in attacking other
problems of a similar nature
and of greater significance..”
„Programming a Computer for
Playing Chess”

10. Dr. Dietrich Prinz in England
wrote the first limited
chess program in 1951.
Alex Bernstein, an
experienced chess player
and a programmer at
IBM, wrote a program in
1958 that could play a full
chess game (although it
could be defeated by
novice players).

11. ●W 1968 roku David Levy założył się o 1,250
funtów, że w ciągu 10 lat żaden komputer
nie będzie w stanie go pokonać. W 1978
wygrał ten zakład pokonując najszybszy w
tym czasie program Chess 4.7

12. ● 1983 US Master rating in - $5000 goes to Ken
Thompson and Joe Condon, when their Belle
● $10,000 for the first program to achieve a
USCF 2500 rating (players who attain this
rating may reasonably aspire to becoming
Grandmasters) was awarded to Deep
Thought in August 1989
● $100,000 for attaining world-champion
status remains unclaimed.

13. In 1977 at Bell Laboratories, Ken Thompson and Joe
Condon took the brute force approach one step
further by developing a custom chess-playing
computer called Belle. By 1980 it included highly
specialized circuitry that contained a “move
generator” and “board evaluator,” allowing the
computer to examine 160,000 positions per second.
This approach was so effective that in 1982 at the North
American Computer Chess Championships
(NACCC), this $17000 chess machine beat the Cray
Blitz program running on a million supercomputer.

14. W 1989 programy były wciąż za słabe aby pokonać mistrzów świata. Garry Kasparov bez
●
problemu pokonuje wszystkie.
W 1996 Deep Blue pokonuje Kasparova (1 z 6 gier)
●
„The Association for Computing Machinery (ACM), the first society in
computing, announces a six game match between World Chess Champion Garry
Kasparov and the top-rated chess program DEEP BLUE. A $500,000 prize fund
has been established for the match to be held in Philadelphia, Pa., USA, Feb. 10-
17, 1996, as part of ACM's year long 50th Anniversary Celebration which begins
with the ACM Computing Week '96 conference. The winner will receive $400,000
with the remaining $100,000 going to the loser.”

18. Part 2:
Implementation

19. Evaluating Function:
1) The relative values of queen, rook, bishop, knight and pawn are about
9, 5, 3, 3, 1, respectively.
2) Rooks should be placed on open files. This is part of a more general principle that
the side with the greater mobility, other things equal, has the better game.
3) Backward, isolated and doubled pawns are weak.
4) An exposed king is a weakness (until the end game).
f(P) = 200(K-K') + 9(Q-Q') + 5(R-R') + 3(B-B'+N-N')
+ (P-P') – 0.5(D-D'+S-S'+I-I') + 0.1(M-M') + ...
K,Q,R,B,B,P are the number of White kings, queens, rooks, bishops, knights and pawns
on the board. D,S,I are doubled, backward and isolated White pawns. Weakness – ie.
exchange of queens

20. ”Type A” strategy considered all possible moves to a fixed depth.
A world champion can construct (at best) combinations
say, 15 or 20 moves deep. Some variations given by Alekhine
("My Best Games of Chess 1924-1937") are of this length.
Let M_1, M_2, M_3, ..., M_s be the moves that can be made
in position P and let M_1P, M_2P, etc. denote symbolically
the resulting positions when M_1, M_2, etc. are applied to P.
Then one chooses the M_m which maximizes f(M_mP).
A deeper strategy would consider the opponent's replies which
are minimized...

21. “Type B” strategy used chess knowledge to explore the more promising
lines to a greater depth.
1) Examine forceful variations out as far as possible and evaluate only
at reasonable positions, where some quasi-stability has been
established.
2) Select the variations to be explored by some process so that
the machine does not waste its time in totally pointless variations.
A crude definition might be:
● g(P) = 1 - if any piece is attacked by a piece of lower value or by more
pieces than defences or if any check exists on a square controlled by the
opponent
● g(P) = 0 otherwise
Using this function, variations could be explored until
g(P)=0, always, however, going at least two moves and never
more, say, 10.

22. (...) the machine once designed would always make the same move in the
same position. If the opponent made the same moves this would always
lead to the same game.
(...) the opening is another place where statistical variation can be introduced.
It would seem desirable to have a number of the standard openings
stored in a slow-sped memory in the machine. Perhaps a few hundred
would be satisfactory.
(...) program based on "type position" could not be constructed. (...). Although
there are various books analysing combination play and the middle
game, they are written for human consumption, not for computing
machines.
(...)To program such a strategy we might suppose that any position in the
machine is accompanied by a rather elaborate analysis of the tactical
structure of the position suitably encoded. This analytical data will state
that, for example, the Black knight at f6 is pinned by a bishop, that the
White rook at e1 cannot leave the back rank because of a threatened mate
on c1, that a White knight at a4 has no move, etc.;
- These data would be supplied by a program and would be continually
changed and kept up-to-date as the game progressed.

23. 1) Board description
and move generation
2) Tree searching/pruning
3) Position evaluation

24. DeepThought bit boards

25. Opening Books: a listing of known good move sequences
that can be used at the beginning of a chess game.
Endgame Databases: a catalog of previously analyzed
board configurations near the end of a chess game that
allows the program to play the remaining moves of the
game flawlessly.

26. A way to quickly check a catalog of positions already examined, which
cuts down the number of positions that must be searched.
12 different piece types {K,Q,R,B,N,P,–K ... –P} on a 64-square board.
Thus a set of 12*64 unique integers (plus a few more for en passant
and castling privileges), {Ri }, may be used to represent all the
possible piece/square combinations. Zobrist (1970)
Pawn hash table - similar technique can be used to store an
evaluation of the pawn structures in a position. Since the number of
pawn positions examined is generally much smaller than the total
number of positions searched, the pawn hash table has a very high
hit rate, allowing a program to spend more time on sophisticated
pawn evaluations because they are reused many times.

27. A model chess program has three phases to its search:
1) Typically, from the root node an exhaustive examination of
layers of moves occurs,
2) This is followed by a phase of selective searches up to a
limiting depth (the horizon).
3) An evaluation function is applied at the frontier nodes to
assess the material balance and the structural properties of
the position (e.g., relative placement of pawns). To aid in this
assessment a third phase is used, a variable depth
quiescence search of those moves that are not dead
(i.e., cannot be accurately assessed).

29. (..., Pb2; Bxb2, Pc3; Bxc3, Pd4; Bxd4, Pe5; Bxe5)

30. Alpha-Beta searcher with enhancements like:
● aggressive futility pruning [19, 31],
● internal iterative deepening [3, 33], *
● dynamic move ordering (history+ killer heuristic) [2, 16, 30, 32, 35], *
● recursive null-move pruning [7, 15, 17], *
● selective extensions [3, 6],
● interior-node recognizers [18, 34],
On average, all enhancements taken together reduce the effective
branching factor of DARKTHOUGHT to 2-3 and its search-tree size to
roughly 55% of that of the according minimal tree [21]. These
observations are consistent with reports by other researchers [6, 38],

31. Iterative Deepening: a technique that gradually
increases the depth of the search tree (the number of
moves and counter-moves) that is examined, rather than
searching to a fixed depth. This allows the most efficient
use of the limited time each player is given to choose a
move.

33. DarkThought implements a standard node-expansion procedure whose structure probably does not differ much
from that of other chess programs:
• extension decisions related to incoming move (U),
• internal iterative deepening (U), *
• transposition-table lookup (L/U), *
• interior-node recognizer decision (L/U),
• endgame database lookup (L/U), *
• null-move search with deep extension decision (U), *
• single-reply extension decision (U),
• futility pruning decision (F/L),
• static evaluation (F/L),
• search of all outgoing moves (L/U).
1. hashed move from transposition table (L/U), *
2. capture moves in MVV/LVA order (L/U),
(most valuable victim / least valuable aggressor)
3. killer moves (U), *
4. history moves (U), *
5. statically presorted remaining moves (U).
F means "frontier" (depth = 1), L "lower part" (depth < 0), and U "upper part" (depth > 1)

34. .. produce a cutoff by assuming that a move that produced a cutoff in
another branch at the same depth is likely to produce a cutoff in the
present position, that is to say that a move that was a very good
move from a different (but possibly similar) position might also be a
good move in the present position.
By trying the killer move before other moves, a game playing program
can often produce an early cutoff, saving itself the effort of
considering or even generating all legal moves from a position.
A generalization of the killer heuristic is the history heuristic. The
history heuristic can be implemented as a table that is indexed by
some characteristic of the move, for example "from" and "to"
squares or piece moving and the "to" square. When there is a
cutoff, the appropriate entry in the table is incremented, such as by
adding d² or 2d where d is the current search depth. This
information is used when ordering moves.

35. current position is so good for the side to move that best play by the
other side would have avoided it. The faster the program produces
cutoffs, the faster the search runs. The null-move heuristic is
designed to guess cutoffs with less effort than would otherwise be
required, whilst retaining a reasonable level of accuracy.
The null-move heuristic is based on the fact that most reasonable
chess moves improve the position for the side that played them.
So, if the player whose turn it is to move can forfeit the right to
move (or make a "null move" - an illegal action in chess) and still
have a position strong enough to produce a cutoff, then the current
position would almost certainly produce a cutoff if the current
player actually moved.

36. • Material-balance scoring,
• evaluation flitihty-pruning decision,
• Pawn-structure scoring (H),
• Pawn-related King scoring (H),
• race scoring of passed Pawns,
• PST piece-placement scoring,
• positional futility-pruning decision,
• flirt her positional scoring (H).
For all steps marked with an H, the evaluation infrastructure
automatically hashes the computed scores.

38. https://github.com/yangboz/godpaper

40.  Thank you!