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Chess Thinking Process

mercredi, 18 juin 2003 • 15:13MarioSpina Commentez ici

"Chess is a mental sport, but there are 1,000 chess books that teach you what you should know for every one that concentrates on showing you an effective way to think."

Learning from Dr. de Groot

There are two areas I would like to research:

1) How do players learn/improve at chess?

2) How do players think during their move?

It may seem that these two areas are only remotely related, but there is more to it than meets the eye. When you learn to play baseball, you don’t just learn about innings, outs, and bases – you are also taught how to bat, to throw, to catch, and to run the bases. But when players learn chess, the only things they are usually taught – even by competent beginner books – are the basic rules such as checkmate and draws, and how to move the pieces. Then they are taught more and more about what the pieces can do. A few guidelines like “At the start of the game try to control the center,” “For your first move, push a pawn two squares in the center,” or “Knight on the rim your future is dim” are thrown in for good measure.

But chess is a thinking game, and almost no beginner is ever taught how to think to arrive at their move. No wonder everyone learns their “chess thought process” in a very mish-mosh way that quickly leads to bad thinking habits. I have explored this phenomenon in many Novice Nook articles and even pre-Novice Nook ChessCafe articles like The Secrets of Real Chess. This month we will examine what I have learned from repeating the experiments of the first and most incisive researcher into thought process, Dr. Adriaan D. de Groot. I have been administering his thinking process “protocol” experiment for over 30 years!

Back in the late 1930s, Dr. de Groot, a professional psychologist and chess master, decided to record the thought process of dozens of players of all levels, with the purpose of laying out how everyone thinks, and what are the differences between the thought processes of varying skill levels. Players were given a position and asked to find a move as well as simultaneously verbalize everything that was going through their mind. These recorded processes were called protocols.

After recording dozens of these exercises, de Groot analyzed the protocols in a very scientific manner. The result was a PhD-type thesis that was eventually translated into English as the book Thought and Choice in Chess, now out of print. In that sense Thought and Choice in Chess is not really a chess book at all, but a non-layman psychology book about chess. It is a tough but fascinating read, and I persevered enough to plow my way through it in the late 1960’s.

Dr. de Groot was able to garner many of the best players of the day to participate in his experiment: Alexander Alekhine, Reuben Fine, Dr. Max Euwe, Paul Keres, and right on down the line to class players. He wanted to find out how they arrived at a move in a typical tournament setting, not to show them “White/Black to Play and Win” positions since the thinking process for well-defined problems is much different:

In “Play and Win” problems you are not just looking for the best move – you need a move that has a forced win in all variations. If you don’t find one, then you try another move. In these problems you don’t give up until you find a solution because you are told there is one!

In “Normal” positions, you need to find the best move in the given time available, just as in a real tournament game. Weaker players often do not take all of their time for game, but for stronger players the opposite problem is often evident: proving the best move on each move throughout the game often takes more time than the clock allows. Therefore strong players must use time management and practical alterations of their maximal thought process to allow them to play all of their moves in the allotted time. This time limitation is one thing that makes chess a sport, as opposed to a “science” where you can take as much time as you wish to prove what is necessary.

Therefore Dr. de Groot selected interesting positions from some of his games and asked the players to come up with the best move in a reasonable amount of time, as if they were playing an important tournament game. He tried to select “rich” positions that would separate the men from the boys. de Groot chose dozens of positions, but most of his work was centered on three, which he labeled Positions “A”, “B”, and “C”.

As a result of this fascinating experiment, Dr. de Groot was the first to prove that, contrary to popular belief, grandmasters do not think deeper than just good players, say those at USCF “expert” level: 2000-2199. What separates the GMs from players at that level is not just the incisiveness of their search – they do it much more accurately and efficiently – but also the number of positions GMs already know how to play: GMs know how to play roughly 100,000+ positions, while masters know only 10,000+. It is much easier to know something than to figure it out. I might add that GMs also evaluate positions much better, as we shall see.

Soon after I purchased the book I began tape recording the thought process of chess players I knew – I remember doing this in college, but I think I started while in high school. Today I still use these experiments as part of my teaching, but for expediency I usually just manually record most of the key thoughts. I hope it is not immodest to say that over the years I undoubtedly have given the “de Groot Exercises” to many more players than Dr. de Groot! For the most part I use the same exercises that Dr. de Groot gives in his book, so that immediately after the exercise I can read to students how Dr. Max Euwe or others thought about the same position, and then to compare the student’s thought process to the World Champions’. When I repeat a de Groot exercise with the same student, I of course use different positions, occasionally ones from my own games.

I have also developed a “halfway” exercise I call the DAT-SCAN, which is “Dan-Assisted Thinking”, where I have a student think out loud to find a move but, instead of no interruptions as in the de Groot exercise (where the subject’s thinking process must only be heard, but never affected), in a DAT-SCAN I proactively help them go through a reasonable process.

What have I learned from giving the de Groot exercise hundreds of times? First, I can almost recite Dr. Euwe’s 15 minute verbal analysis of “de Groot Position A” from heart! I have even corrected the typos and de Groot’s stenographer’s mis-recordings of what Dr. Euwe meant. They did not have tape recorders (or today’s digital recorders!) in 1938.

On a more practical side, the following conclusions stand out:

1) No matter what process they think they use, players rated below 1600 USCF almost all practice what I have called “Hope Chess”: they make moves without considering whether they can deal with all the threats their opponent might make next move. To put it another way, they often make a move without at all considering the consequences of what might happen when they do. They make a move without a Principal Variation (PV) – not wondering or looking to see if their move allows an opponent check, capture, or threat on the next move that might win the game immediately. This was not a surprise to me; it is very difficult to have a rating that low if you have reasonable chess knowledge and play “Real Chess” every move of each slow game you play.

As I noted in my Reader Question in a recent Novice Nook, one student recently e-mailed me:

“I identified myself as a "Hope Chess" player when previously I had fooled myself into thinking I was largely past that phase. However, as with the weakest link, if I play hope chess on any move, the game is hope chess. Loosely analogous to 12-step programs, the first step to recovery is to admit that I am a hope chess player.

Yesterday's walk through my game where I had ignored many relatively simple responses (and only won because my opponent was equally lax) was a terrific eye- opener, as was the De Groot exercise.”

2) In the majority of positions that occur during a normal game, at least some pieces from the opposing forces can interact. For these positions I advise a student to first consider their most forcing moves - checks, captures, and threats - when identifying candidate moves. However, many fail to realize how important this advice is and don’t do so systematically. Weaker players jump all over the place, rarely covering all the pertinent lines, such as considering all their checks or captures, much less all possible opponent recaptures after a capture. For example, after a capture that allows multiple recaptures they often just assume a recapture, never asking “If I were him, which recapture would I really choose?” This mistaken assumption often leads to conclusions that prove very little. Good players don’t make this kind of mistake, especially in clear positions where they can work out everything with a little effort.

3) While weak players analyze much worse than strong players, it is the evaluation skills that separate the 1800-2100 players from the international players. GM’s see immediately which positions are good or bad, and worth further consideration. 1800-2100 players analyze fairly well, but arrive at much less accurate conclusions!

Evaluation is a large part of what separates upper echelon classes from each other, such as grandmasters from experts. Often players rated 1900-2100 used the same deductive logic that GMs did in deciding what is forced and what is not, and what is likely to happen. But then, at the end of the lines, when the GM’s might conclude “That is good for me – if I don’t find anything better I will certainly be happy with that!” the 1800-2100 player might look at the same position and say, “I am not winning any material and I don’t see anything special, so I will probably play another move.” So the key is that players rated 2400+ are not extremely better in analysis than the players in the 1900-2300 range (don’t get me wrong; top players are more accurate and less error-prone!), but are clearly much better in evaluation, especially evaluation of “even material” positions. Therefore GMs and IMs are much more likely to be able to choose the best continuation among several alternatives when the obvious evaluation criteria of king safety and material are not big factors. Moreover, weak players almost always value pawn structure above initiative and the entire army’s activity (see my recent Novice Nook Evaluation Criteria), while strong players don’t care nearly as much about the pawn structure if they can maintain a clear initiative.

4) Weaker players often evaluate “potential” tactical moves primarily on material, but non-tactical moves on positional grounds. This seemingly minor nuance often turns out to be an unbelievably big mistake! For example, they will reject a capture because “it does not win material”, but instead play a quiet move that often fails to force play, allowing the opponent to seize the initiative. Apparently they feel that when a capture fails to win material it does not succeed in its primary purpose, and thus for that reason alone give it a lower evaluation than a non-capture, even if the resultant material is otherwise even in both cases! Of course, this is bad logic; it is entirely possible that the capture leads to a better position than the non-capture. A part of chess skill is seeing how captures which don’t change the material balance might prove favorable by trading your bad pieces for his good ones, eliminating key enemy defenders, keeping the initiative, etc.

5) Some players spend a ton of time looking at lines that are not forced and almost never could happen. They don’t use deductive logic to think, “Suppose I do this – would he really do that? And if so, what would I likely do?” One of my college chess teammates, who shall remain nameless, looked an astounding 40 ply (!!) or so ahead with perfect board vision, saying “Suppose I do this and then he does that, then I will probably do this and suppose he does that and then I do this...” However, the ten minutes he took to do so was a complete waste of time because not only was the initial move he was contemplating not necessarily best, but none of the subsequent moves were either. He made no attempt to show that the moves under consideration were best or forced, or why he or his opponent would play them. I would estimate the chances of that entire line occurring as much less than 1 in 1,000,000,000 – completely worthless! It is much better to spend time analyzing moves and evaluating lines that occur early in the search and that might take place, rather than spend time analyzing moves deep in the search that almost never could happen – and even if such deep, non-forced lines did occur, you could always analyze them during later moves!

6) Most students love the de Groot exercise and consider it most revealing, while others find it hard to take for the exact same reason: because it so pointedly shows what they are doing wrong. A few months ago I gave the de Groot exercise to an “A” player who made a clear thinking process error that was easy for me spot, due to its clear differentiation from what the GMs did. The knowledge of this serious error in his thought process may have been enough in itself to prevent him from becoming an expert. However, after he was finished and I explained what he did and how that differed from GM protocols, he became indignant and vociferously defended his process. Even when the exercise works and is insightful, it is sometimes unfortunately inciteful!

7) Weaker players don’t statically evaluate unfamiliar positions. I define evaluation as looking at a position and determining who is better, by how much, and why, and define static evaluation as evaluation done before analysis. Many weaker players might mention that one side has an isolated pawn or a weak square – one might call this an assessment - but, without the final who is better, by how much, and why, there is no conclusion turning that assessment into an evaluation. Players rated below 1800 rarely include in their protocol anything similar to the following:

“The material is even, the Kings are about equally safe, White has a better pawn structure, but Black seems to have more total piece activity. Since it is Black’s move, I think he can take advantage of that activity, so I like Black much better.”

Due to your knowledge of prior play, you don’t need to make an evaluation before each move when playing a real game but, when starting a de Groot exercise, it is helpful begin with an evaluation. Instead, weaker players often begin their thought process by either making a general assessment with no conclusion or, worse, immediately searching for candidate moves; many do not even count the material. That does not make much sense because, without an evaluation, how do you know what you might be looking for?

As a trivial example of how an evaluation is helpful, suppose you find a forced draw – would you take it? If you think that otherwise you are losing, you would probably be very happy with the forced draw (in which case you were not worse at all, were you?). But if you thought you were winning, why would you settle for a move that forces a draw? So knowing which side you think is better provides some goals as for your analysis. For a further discussion on this, see my pre-Novice Nook ChessCafe article Using Steinitz’ Laws.

8) Another thing a weaker player should do, but doesn’t always, is assess the threats generated by an opponent’s previous move. Since in a de Groot exercise you are not given the previous move, then one needs to look at all threats (in a real game you can often shortcut this process by only considering the new threats). The way to do identify the threats is to think, “Suppose it were not my turn, but again my opponent’s, then what would he do?” Doing this also helps you find all the opponent’s “killer” moves, which are very strong threats that cannot be ignored. Killer moves can eliminate your candidate move from consideration if that candidate move does not meet the strong threat. As a simple but strong example, suppose you see that if you do nothing on the next move, your opponent can play 1...Qh3 with unstoppable mate threats on g2. Then any candidate move that allows 1...Qh3 and then mate must be discarded, and only moves that can meet or prevent this threat are worthy of further consideration.

9) Does a player who plays too quickly slow down because he:

A) Acquires additional chess knowledge and has more to think about, or because he

B) Knows he can play a lot better if he plays slower?

De Groot exercises showed me that for many, A is not the primary reason, although both usually apply. I rate the weight between these two as approximately 35%-65%, Reason B predominating. For example, suppose I teach someone "X" things they should think about every move, but they still play so fast that they could not possibly be thinking very much about those X. Then surely adding "Y" additional factors to consider so they now have “X+Y” will not slow them down - it might intimidate them so they are even less likely to think about the X! It is clear that for players who do “X” that Reason B is correct - they need to "buy" into the correct thinking process in order to slow down - teaching them more things to consider during their move gets severely diminishing returns, if not negative.

Sometimes a player’s motivation to slow down depends on how much fun the extra or correct thinking is, and how much he wants to improve and is willing to do the work. Once you are aware of what is involved, it is not magic to begin to practice a good thought process – and doing a minimal amount every move religiously is required for high level slow play.

One thing that often slows down players who play too quickly is peer pressure. Over-the-board players seem to learn to slow down better than internet-only players because they go to strong tournaments and see all the good players taking their time! This sometimes works wonders, as adults don’t want to be the first player done each round, and thus learn to “imitate” the time management of those around them.

10) Even intermediate level tournament adults don’t always follow the advice, “If you see a good move, look for a better one – you are trying to find the best one.” Often they just calculate to see if their intended move is reasonable and, if so, they immediately make that move. But this is also a big mistake. On most moves that require analysis, the goal of your thought process is to prove that you have found the best move, not to show that a move that attracts you is reasonable! Proving that a move is reasonable is not an efficient way to find the best move, and is also a reason why some players play too fast.

In Thought and Choice in Chess de Groot identifies four phases of the though process of stronger players and calls them: 1) Orientation to Possibilities, 2) Phase of Exploration, 3) Phase of Investigation, and 4) Striving for Proof. Weak players rarely go through all – or sometimes any – of these phases. And intermediate and weaker players almost never strive for proof, “proof” meaning they have systematically gone through the process of showing that the move they are about to play is better than any of the other candidates (that is leads to a better position, by force, than the others do), and thus is really the best one.

In addition to the lessons I learned from administering de Groot exercises, what should the average chess player learn as well?

After doing a de Groot exercise, students who listen to Dr. Euwe analyze the same position always get an eye-opener! But, despite the imposing depth of Dr. Euwe’s thorough analysis, it is actually quite easy to emulate his process. Thus everyone who hears this process can and should strive to do something similar. The hard part, of course, is not emulating the process, but picking up all the extra knowledge that allows one to analyze and evaluate well, and to get good results from it! It takes years of good practice and judgment refinement to be able to evaluate a position well, and more years to be able to do so with anywhere near the sophistication of a GM, even if you have the capability. It does not take nearly as long to learn how to analyze well, but even that is almost always measured in years, and not weeks or months, as so many players would wish. The amount of work it takes to analyze well is much higher than most players realize, or possibly even find fun. After students listen to all the work Dr. Euwe did to find the best move, some of them wonder “Do I really want to do all that?!” That is a reasonable reaction: If you do not find extensive analysis and delicate evaluation fun in positions that demand it, then you probably won’t do it now or ever. However, unless you change your preference, your chances of ever becoming a very strong player are likely nonexistent.

The average player also often realizes that that the gap between him and top players is larger than previously imagined. Many players fool themselves into thinking that if they studied more openings and endgames for a few years that they could or should or would eventually move up 1000 rating points or so. I think doing the de Groot exercise is an epiphany that shows that there are more important things to do in order to get really proficient at chess than just learning some new moves in the Caro-Kann or rook-and-pawn endgames. Not to say that opening and endgame study isn’t important, but how many players do you know that have played 10+ years, read 100+ books, can quote chapter and verse on book knowledge, and yet are still rated 1500 or not much higher? Without even testing them, I can tell you that these players have a poor thinking process and will never get much better until they correct it.

Finally, I get a lot of inquiries about what is a minimally correct thought process for a typical slow game position, as opposed to the detailed process I discussed in the Novice Nook A Generic Thinking Process. There is no one correct answer, but here is a try:

1. After your opponent’s move, ask yourself “Why did he do that?” and “What are all the moves he can do which he could not do before?” Concentrate on opponent’s moves and ideas that can really hurt you. Obviously if he made a check you need to get out of it, and if he made a capture you likely need some sort of recapture, possibly next move after a zwischenzug (in-between move). However, if his move is not a check or capture, look for the threats it created. These are found by asking, “Suppose I pass and he just moves again. What could he do to me that I would not like?”

2. To begin looking for your move, consider moves that meet his threats, as well as your own checks, captures, and threats. If there are none of consequence, consider the plan of making your army more active, especially identifying your piece which is doing the least and finding a move or plan which makes it do more or, conversely, moves that restrict your opponent’s mobility. Another plan is to find moves that take advantage of opponent weaknesses or your strengths. Don’t waste time on grandiose plans that are not, to paraphrase IM Silman, both feasible and effective. Discard potential threatening moves that are easily met by opponent replies that leave your position worse than before. The list of reasonable moves you generate are called candidates.

3. Find the checks, captures, and threats that your opponent could reply after each candidate. If he can make even one move that you cannot meet, then that candidate should likely be discarded.

4. For each of the remaining candidates, assume your opponent will make his best reply (not an easy task!) and try to figure out what (short) sequence is likely to occur. Visualize the position at the end of that sequence and evaluate it. In order to evaluate a position, it usually should be a quiet one and not in the middle of a checking or capturing sequence. For unclear sacrifices you just have to use your experience and judgment. Do not make the common mistake of evaluating the position immediately after the candidate move, ignoring opponent’s replies and how to meet them! If your sequence is reasonable (for both sides), the evaluation at the end of the sequence should also tell you how much you like that candidate move.

5. If you see a good move, look for a better one! After performing #4 for each candidate, compare the evaluation of the resulting position with the evaluation of the best position you have found so far, the “king of the hill”. If the new move’s position is even better, it becomes the new king of the hill.

6. Once you have finished evaluating all your candidates, your move of choice is the one that starts the sequence leading to the position that was the final king of the hill! The sequence of moves you found for that “best” move is called the Principal Variation (PV). A PV is the sequence that ChessMaster 9000, Fritz, and other chess software engines display as their top analysis line. Your evaluation of the positions in the PV also becomes your dynamic evaluation of the current position. For example, if you see that the PV wins by force, then your current position must be winning!

7. Do a sanity check. Over the board, you can write down your move, close your eyes, and/or take a deep breath. Re-examine your move with fresh eyes. Is it just crazy? Does it leave a piece en prise? Miss a mate? You should not try to redo your entire analysis of the written move. If the move is crazy, cross it out and reconsider, starting with the “second-best king of the hill”. If it is not crazy, make the move, hit the clock, and write down your time remaining for the game beside your recorded move.

The above process would not be used for all kinds of moves. Exceptions include: the only moves to get out of check, book moves, mating with a Queen and King vs. King, etc. But for moves where real decisions have to be made, it is not a bad outline. Hope this helps!

par Dan Heisman, juin 2003 pour Novice Nook @ ChessCafe

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Chess Clock - DGT XL

mardi, 17 juin 2003 • 23:06MarioSpina 2 Commentaires

Nos merveilleux amis de DGT viennent tout juste de lancer leur nouvelle horloge digitale haute-gamme.

Sexy hein ?

Les nouveautés:

  • L'affichage est 50 % plus grand. Les chiffres ont maintenant 18 mm de haut.
  • L'information affichée est plus complète. On affiche:
    1. Le mode chrono utilisé
    2. La période de la partie en cours
    3. Des cercles indiquent qui joue avec les blancs et qui joue avec les noirs.
    4. Un icône indique si l'alarme est en fonction
  • Une alarme indiquant les points de contrôle de temps peut être utilisée
  • Deux bouttons ont été ajoutés:
    1. Un boutton "-1" pour faciliter les corrections de temps et les ajustement
    2. Un boutton "go back" pour faciliter la naviguation entre les chiffres
  • Le boutton de départ-arrêt a été placé au centre
  • Le nombre de batteries nécessaires a été réduit a 2 piles AA
  • Le mécanisme du levier a été renforcé. Il peut supporter au minimum un million de mouvements
  • Un nouveau mode de contrôle de temps a été ajouté: "Upcount"
  • Il est possible de conserver 5 contrôles prédéfini en mémoire.
  • Il est possible d'utilisé plusieurs mode dans une partie (ex: 1er période: normale; 2e période: Fisher)
  • L'Horloge peut-être branchée au DGT Electronic Board et communiquer avec un PC dans les 2 directions:
    1. Les données de l'horloge sont communiqués à  l'ordinateur
    2. L'ordinateur peut envoyé des données d'affichage à  l'horloge. Par exemple, l'horloge peut afficher les coups joué par l'ordinateur lors d'une partie. il n'est plus nécessaire d'utiliser le mode "Vocal" de Fritz.
    3. il est possible de synchronisé les horloges d'un évènement par internet.

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Échecs: Humain / Machine

lundi, 5 mai 2003 • 23:20MarioSpina Commentez ici

Voici un très bon article faisant le sommaire des derniers évènements Humain / Machine de cette année. Il donne aussi un bon apperçu de l'évolution du jeu des machines. J'inclus le lien vers le site, mais j'en ai tout de même fait une copie dans la section 'extended' pour mes archives. C'est un peu long comme article, mais ça permet de faire le tour de la question rapidement.

X-bit labs - Articles - Chess Championship: Humans vs. Computer

Chess Championship: Humans vs. Computer

by Lev Dymchenko

05/04/2003 10:41 PM

During the last 10 years
computers penetrated into various spheres of human life. In the today’s article
we will try to find out how well computers can play chess and if it would be
correct to say that artificial intelligence is superior to human mind.

It’s now impossible to imagine our civilization without computers: they are virtually everywhere. Chess is one more field where computers do quite well.
With the arrival of chess programs and the growth of computers’ calculating
capacities, they started talking about the dawn of Artificial Intelligence.
That’s what we are going to discuss here: the computer in chess or computer
chess.

Clone Attack

In the last decade computers became an indispensable tool in chess masters training. It was becoming hard to operate
huge databases of opening variations and chess games without personal
computers. Today, a PC with a personal openings library is one of the
attributes of a chess master. The computer was gaining its ground slowly, but
surely. Kramnik is said to have started using computers the last of all super
grandmasters. When his more “advanced” colleagues were all into chess software,
he was still scribbling variants down into an old copybook. But eventually he
couldn’t resist the progress, either.

With the arrival of chess software and powerful PCs, chess openings were studied more effectively. You could make the computer analyze a position and
use the results of the analysis later. A lot of popular openings are now traced
to deep endgames. The theory of openings reaches dozens of moves into the game
and a novelty on a twentieth move is no surprise today, unlike the novelty on
the tenth move, where everything seems to be studied inside out already.
However, we will see that it is not so true yet.

Many known players are criticized for winning games by using computers at home and thus having wider knowledge of game starts. They often use new
untypical moves in the beginning of the game. Kasparov is a player of the kind.
On the one hand, his style means getting advantage right from the start. On the
other hand, he gave cause for this criticism himself having pioneered the use
of computers in training.

But the problems of analytical work of professional players are not so interesting for ordinary chess fans. It’s quite more fascinating to watch a
battle between chess programs and a man. And that’s what we are going to talk
about.

There have been two remarkable events in the last two months: two Man vs. Computer matches with much publicity and a round sum of prize money. First, the human world champion Vladimir Kramnik met Fritz, the
world champion among chess programs, in an eight-game match. Then it was the
turn of Kasparov, who is the leader of the FIDE rating list, to play six games
against Junior program. Junior had also been a world
champion earlier. These two chess programs haven’t met with each other so we
can’t say which one is stronger until the next computer world championship. We
know that these two programs play different chess, though. Fritz is inclined to
positional game, while Junior likes attacking. It was said that Fritz resembled
Kramnik’s style, while Junior – Kasparov’s. So, the grandmasters played against
their electronic incarnations.

As our web-site is about computers, I have to write specifically about the successes of the electronic brain, but I suppose that the reader knows the
names of Kasparov and Kramnik. If not, there is a link at the end of the
article to a literary description of the inhabitants of the chess kingdom

Man vs. Computer: Match History

So, let’s see how flesh-and-bone chess masters fought against artificial intelligence. We will start out with the sensational match Kasparov played
against Deep Blue, a chess super-computer specially built by IBM Corporation.

This happened in 1997. Personal computers were too weak then to compete with human grandmasters, so IBM made a giant multi-processor chess computer. The
first version of the machine was far from perfect and was routed completely by
Kasparov. But the second version managed to beat a human world champion in a
new match. This provoked a big commotion and drew wide publicity. So what
happened then? Let’s figure it out.

There were six games. Kasparov played White in the first one. Kasparov chose to use “queer” openings in almost all the games so that the computer couldn’t
use its openings library. He made strange waiting moves: you don’t play like
that against humans because this is actually a loss of initiative. Anyway,
Kasparov got an advantage in this game, profited by positional mistakes of the
program and eventually won.

The second game was Kasparov’s Black. The computer was surprisingly good this time. It made a series of strong planned positional moves and threatened
Black’s fortifications. We will discuss the operation principles of chess
programs later in this article. For now let’s state that programs are not very
good at positional playing. They process separate moves, not move sequences or
plans. Kasparov tried to save the day. He sacrificed two pawns and got some
chances to attack the White king and achieve a perpetual check. And there was a
miracle: the computer took 15 minutes to consider the next move, although it
had made all its previous moves in three minutes exactly. After that, it declined
the sacrifice! All chess programs are in for material things. They don’t see
positional threats, but they can count up pawns. That’s why one of the common
methods against programs is to sacrifice for a positional compensation, which
cannot be evaluated correctly by the program. Frustrated and confused, Kasparov
moved his pieces around for a while; the computer reinforced its position,
created threats, but somehow allowed Kasparov to check its king and save the
game through a perpetual check. But Kasparov resigned. He immediately learned
from the commentators that he had given up a drawn position – he did have a
perpetual check!

Kasparov said that he hadn’t believed the computer to choose a perpetual check variant when it had declined this very variant a few moves before,
refusing two (!) pawns.

In the next three games Kasparov was pressing on the program, but it always slipped to a draw by means of tactical nuances.

In the last, sixth game, Kasparov played Black and chose a variation where White sacrifices a knight for a sweeping attack. The computer sacrificed the knight
and won easily. After the game Kasparov reasoned that computers didn’t usually
give material for initiative, which they couldn’t evaluate correctly. Some
commentators said that this variant could have been put into the openings
library of the machine. Kasparov somehow dismissed this option. Some said
Kasparov just confused the order of moves. That is, he made a second move of
the variant before the first. This can happen to a chess master, especially, as
Kasparov was quite tired after the previous games.

So, the computer scored 3.5-2.5 in the match. IBM puffed up this success immodestly. I even recall some movie beginning with something like “This was
the year when Computer defeated Man at chess”, a kind of apocalypse. IBM’s
stock went up significantly after the match.

The strange behavior of the computer in the second game made Kasparov’s team doubt the fair play of IBM. They suggested that IBM had invited a grandmaster
to prompt certain strategic moments to the chess program. That is, the helper –
a human – told Junior not to accept the sacrifice of two pawns. This would have
brought the computer under attack, which it couldn’t have calculated to the end
and evaluate right. But when the computer gained a critical advantage, the
advisor lost concentration and allowed Kasparov’s perpetual checking.

Well, the company was very interested in the match result. The computer had lost the first match hands-up, and IBM had put a lot of money into the project,
so there shouldn’t have been any misfire anymore. Another fact: the computer
was dismantled right after the match.

But on the other hand, we could also suspect Kasparov in losing the match on agreement. Why did he resign in the second game without even trying to give a
few more checks? Why did he choose a risky variant in the last most important
game? There are more questions than answers.

The multi-processor computer was also rumored to have hung up a few times, so that it had to be re-booted. The technical staff were
bringing it to its senses like a boxer between rounds. I may venture a theory
that the computer gave out a series of good moves (maybe accidentally,
sometimes a poor player can do a thing or two) and then hung up. And it takes a
while for such a machine to re-boot. And after the re-boot, it wasn’t into the
game yet and played “safe”, avoiding sharp variants, before all its units were
turned on. Anyway, the program did hang up to Kasparov’s astonishment. We will
discuss this game in detail below when talking about chess program algorithms.

Of course, IBM had to dismantle such a reboot-inclined computer as soon as possible. Maybe they would have done this after the first match, but the proud
name of the corporation and the money already spent stopped them. They saw that
it was a hard job to make chess computers and were lucky to wind their project
up with a loud bang.

If we take pure chess into account, the AI showed nothing extraordinary for a world champion: the last game was an evident blunder of the tired Kasparov, the second should be considered a draw, as the
final position was a draw. In other drawn games, the computer showed absolutely
nothing, just escaping to the safe side. It’s even strange that a specialized
chess computer should play so weak.

I’m far from suspecting IBM in “unfair play” as Kasparov does, but still it doesn’t seem right when an average result and escapes to a draw are presented
as a victory of the artificial intelligence over the human one. It seems like
IBM-compatible computers have made a career to the ranks of IBM top managers.
Of course, this result is a big step ahead in computer chess, but more steps
are required for a computer to reach the level of the human world champion.

Newest Matches

Processors for PCs and small multi-processor systems were becoming more powerful. So, chess programs, which are also constantly polished off, can now
show grandmaster-level play without running on a super-computer.

First, let’s discuss the earlier match – Kramnik vs. Fritz. It was eight games long and somewhat split up into two halves. In the first four games,
Kramnik was accurately following a well-thought anti-computer strategy. He
exchanged the queens and went for a complex endgame with most pieces still on
the board. There were fewer tactical variants there, while the big number of
pieces prevented Fritz from considering variants deeply enough to see the
outcome of its decisions. For example, the computer can consider a variant 6
moves ahead, but the outcome of the variant will happen on move 10. In this
case, the computer can make a weak move.

When there are no queens, time slows down in chess. Everything goes on less fast and it’s often necessary to make a number of simple, but plan-bound, moves
to achieve success. To cut it short, Kramnik won two games, and in one game the
computer managed to draw thanks to Kramnik’s inaccurate use of his advantage.
In the first game of the match Fritz had a better endgame, but to no effect: it
just couldn’t play it right.

The second half of the match was quite contrary. Kramnik must have been bored to play and win again and he lost a knight and resigned immediately. In
the next game he was going to attack. He sacrificed his bishop for a pair of
pawns and drew the Black king to the fore. But he didn’t manage to end the
attack. Computers are strong in calculations-heavy positions, while Kramnik
must have made a mistake somewhere. Nevertheless, the initiative turned to be
enough to get an endgame with some chances for a draw. He could build a castle
when the Black queen couldn’t pass a white rook’s defense – the computer can’t
see such things and allows them. But Kramnik followed a kind of tradition of
human players in such matches and resigned in a nearly drawn position. After
that he lost all his vigor. The last two games were insipid and ended in draws.

I could try to represent the match as a boxing event. Kramnik had his opponent on the floor for three rounds. In the next round he got pressed
against the ropes, missed a simple blow and went down. In the next round he
rushed to attack and nearly knocked the computer down, but overlooked a
counter-attack. Although he could stand on his legs still, he preferred to
report a technical knockdown. The last two rounds were spent in a clinch. In
boxing, Kramnik would have won by points: the iron opponent was not ready for
the fight. 

This was an agreed match meaning that both parties just agreed to play it. If there had been any elimination contest, this version of Fritz wouldn’t have
got to meet Kramnik. It has an evident weak point: complex endgames. The two
games Fritz won were Kramnik’s unprovoked mistakes, rather than Fritz’s strong
play. Those grandmasters that are at the bottom of the chess Olympus,
wouldn’t just play chess to Fritz, they would compete: win a game and then wait
for a mistake of the computer.

In fact, an earlier version of this chess program played with grandmasters at a super-tournament in Dortmund.
At first, humans didn’t know what to do against the machine,
they just tried to defend themselves. Then one of them won a game and thus
uncovered a weak spot of the program. It was weak in closed positions. After
that, they all tore the machine to pieces every game.

Kasparov’s Return Match

Now, we have come to the last event: Kasparov vs. Junior. Although this program has nothing to do with IBM’s creation, this match was considered to be
a kind of rematch.

Game 1. Kasparov won the first game. This time he didn’t go for irregular openings and stuck to classic
schemes. Although the chess program could use its openings library, it couldn’t
solve the problem. Junior had a worse position right after the beginning and
Kasparov had no problems winning the game.

Game 2. Kasparov (Black) played a sharp Sicilian defense and got a better position. At one moment Junior agreed
to a sacrifice for a strong attack, but got a lost position. Kasparov was too
hasty, though, and gave the computer an opportunity to draw the game by
perpetual check.

Game 3. Kasparov was attacking again. But the position was opened and the program stood its ground. Anyway,
the machine had nothing more than a draw. And here Garry forgot the rules when
you play against a computer: he was too engaged into the attack and left his
king undefended. To make the things worse, he missed an opportunity to announce
a perpetual check and was checkmated the next move. Human attention often tends
to concentrate on one thing, this time it was the attack. The computer, on the
other hand, has no such problems: it can easily switch from attack to defense.
I play chess with computers quite a lot and it’s a usual thing for the computer
to threaten my king with its queen and the next move take the queen to the
opposite side of the board to help its own king. Such a defensive move can be
easily overlooked by a human player.

Game 4. Kasparov wanted nothing more than a draw in this game (he played Black) and got a rook endgame without
a pawn. Junior agreed to this variant, not realizing it was a dead draw, in
spite of his advantage in pawns. So, they drew the game.

Game 5. Kasparov was ready to attack, but quite unexpectedly Junior sacrificed (!) a bishop on square h2 for
an attack of his own. Kasparov took a long time to think over his next move and
declined the sacrifice. Later he explained that he could lose such a
tactics-heavy position to the fast calculating program. So, the game was drawn
by repetition.

This game is considered to be a success of the artificial intelligence. A computer exchanged material for initiative. Only humans were supposed to be
able to play like that. But we shouldn’t be hasty with our judgment. The
sacrifice occurred in early opening and may have been put into the program
before the beginning of the match. We shouldn’t forget that a few strong chess
masters were involved into the development of the program and they do know what
openings Kasparov likes to play.

Game 6. In the last game Kasparov once again outplayed Junior, got a complex ending with a big advantage. Some
commentators said it was a technical win. Kasparov preferred to draw it,
though. It was more important for him not to lose the match than win it.

Once again, let’s picture it in boxing terms. The computer was beaten the first round and was very lucky to escape in the second. In the third round it
was cool-headed in defense and punished his over-enthusiastic opponent in a
counter-attack. The next round, it was Junior that couldn’t break Kasparov’s
defense. The fifth round, Kasparov was ready to show his best, but got a nice
punch somewhere at the liver. Before he made up his mind to venture an attack,
the round was over. The last round, Kasparov was sharp and accurate, but took
no chances and let the opponent remain on his feet.

So, the match ended in a draw. But did Junior play stronger than Deep Blue? Deep Blue had a higher computational power, but in the last seven years PCs
have been growing, too. Chess algorithms have also improved over time and thus
make up for a lack of power.

Let’s consider the overall result of Man vs. Machine matches. The computer won 5 games, lost 4 and drew 11. There is no decisive victory among the five
wins, only evident mistakes on the grandmasters’ part. As for losses, they are
real defeats, exposing serious flaws in play. So, it’s quite unclear how a
computer can win without a human team behind it.

You know what kind of a player computer looks to me? There are people who play chess for money at cafes and public parks. They have a lot of learned
opening variations and a number of standard traps. They try to confuse the
opponent with provocative moves and catch him on small tactics, but they don’t
really understand chess.

So, how comes the AI plays just like a chess cheater?

Key Principles of Chess Programs

So, how do the chess programs work and manage to beat the world chess champions? Every chess algorithm is based on a simple search of all
possibilities and selection of the best among them. So, we have a position for
the computer to make a move. It studies all the possible moves one by one.
Every move means a new position where the computer again studies all the
possible moves and so on, to a certain depth.

In the end, it evaluates the positions and chooses the best move. In the case shown on the diagram, it is going to choose the first variant, as any of
the opponent’s moves would give the computer a small advantage.

How does the program evaluate a position, rate it with a number? It bases its decision on factors, which can be obtained analytically. First of all, it’s
the number of pieces on the board. Then, there is the activity of the pieces,
which is the number of their possible moves, control over the center, advantage
in space, and the number of squares under control. There are also simple
factors concerning pawns: double pawns, weak isolated pawns, passed and
retarded pawns. The safety of the king can be calculated according to the
number of squares around it controlled by friendly pieces.

But you should bear in mind that these factors are calculated purely mathematically, mechanically. A computer program can be viewed as a book of
rules in this respect. And this book reads: a double pawn is a weakness. But as
for other factors, they can’t be easily calculated: an isolated pawn can be
either a strong or a weak point of the position, depending on the given
situation, so that all the variants should be taken into account. As for pieces
activity, it is also not always clear if this activity is real or fake, as the
computer simply states the availability of a big number of theoretical moves.

Considering all this, it’s no wonder chess programs are strong in “computed” positions, where they can usually gain material advantage by tactical
maneuvers. Programs just look through all the variants and choose one where
they have more pawns. Human players may turn this against the program itself,
like in the above-described games.

Now, let’s try to estimate the calculation depth achieved by using this simple searching method in modern PCs.

Imagine that we have a 1GHz CPU and 100 seconds per move. That is, the program has 100 billion clock cycles per move. Let’s take that one position
requires 1000 cycles to be evaluated. This is an approximate, and even lowered,
number. We take into account certain optimizations of the search algorithm. For
example, when a position can be achieved by different moves, it’s only
evaluated once. Of course, biggest contribution is made by the variants on the
last level of the variant tree.

Now, let’s estimate the number of possible moves in a typical chess position. When most pieces are on the board, it’s about 30-60. For simplicity,
let’s think it’s the square root of 1000.

100 billion cycles divided by 1000 cycles per move = the number of positions at the deepest calculation level. Find the logarithm of the number of possible
moves. The result is 5-6 half moves. That is, about three full moves. This is
not great. The exponential nature of chess moves search kills the performance.

But maybe we can delve deeper using a faster processor and more time? Here is the table showing numbers for 1000 seconds per move instead of 100 seconds.

CPU

Calculation depth (half moves)

1GHz

6

1000GHz

8

1000THz

10

It’s clear that chess cannot be thoroughly calculated. The only hope for chess programs lies in optimization of their algorithms.

Of course, even candidates for master’s title can calculate 5 moves ahead. So, what did they do to this searching algorithm that it proved capable of
competing with the world’s champions?

Variant Tree Optimization

So, the developers re-shaped this variant tree: somewhere they trimmed it up, elsewhere – made it longer. One of the ideas is to cut a variant short when
any of the two sides gets a critical advantage. This is done to save time and
trouble calculating the moves when the queens of both players take defended
pawns from one another. However, things are not so simple here. This pawns
exchange can be the beginning of a checkmate combination, after which the
material and other positional factors are of no importance any more. That’s why
this “cutting- short” should happen not at once but a few moves deeper.

The second idea goes like “if we can’t calculate all the variants, we will consider only important variants but do it in great detail”. The problem is
that it is not quite clear which variants are important. So, first of all, the
computer counts up all captures and checks in every position. So that it
doesn’t stop considering a variant a few steps short of a checkmate.

Chess programs like Fritz or Chess Master have an option of showing a variant they are now considering and its depth. Usually, this depth is 12-14
half moves. But in fact, the computer has considered a number of variants far
deeper – by tens of moves. Thus, the computer can find a checkmate in 100 moves
if this is a forced variant (that is, a check at every move). And it may not
find a checkmate in 10 moves if the variant consists of “silent” moves, without
captures and checks.

It’s the optimization of a chess program rather than the evaluation algorithm that affects the playing manner. There is a kind of balance between
calculations going wide and deep. If the program dismisses variants too fast it
may miss hidden tactical opportunities, but spend more time evaluating
positions.

Now, let’s discuss a few examples of the chess programs work. We will consider an unexpected tactical blunder of the Fritz program, but before that
take a look at a few moments from the Kasparov – Deep Blue match.

Chess Programs Thinking


Second match between Kasparov and Deep Blue.

Before move 46. Ra6?

The computer played Ra6. Instead of going to this absolutely won ending:



This ending is hopeless for the Black.

They have a lot of weak spots and a great number of passive pieces.

...it gave Kasparov an opportunity to set a perpetual checkmate by 45...Qe3 46.Q:d6, Re8! 47.h4 And there’s no way to escape
the check any more!


The White move now. No protection against
the perpetual check.

How come that a chess program commits such childish mistakes? It’s quite clear even to an average player that it’s better to go to a technically won
position than put your king under the threat of a perpetual check.

Still, most chess programs, even after long thinking, move their rook to a6. They can’t calculate this endgame to the win. And they can’t evaluate it right:
the White have a more active rook and an isolated passed pawn, but the Black
have a protected passed pawn. The bishops have about the same number of
available squares. The program can win this end (if pressed to it) by slow
enhancement of its pieces’ positions. During the realization of the advantage,
the Black may become more active, create more passed pawns – all this cannot be
properly evaluated by the program.

And in the variant with the perpetual check, the program has a big material advantage – a bishop – and a lot of positional pluses. As for the check itself,
the program can only detect it when things get worse in all positions or if
there is a triple repetition of a certain position. And you can wait for dozens
of moves before this triple repetition occurs: the white king can go to the
queen’s side, come back again, try to hide behind the bishop, go somewhere
else. There is an ocean of variants, but they all point to a draw. The computer
can’t understand it. I was trying to run away from Fritz program for 30 moves
in this position.


In this situation the program may calculate certain variants dozens of moves
deep,

and still see no perpetual check indicating a significant advantage of the
White instead.

So, we shouldn’t be surprised at the computer’s choice. By the way, all programs I investigated this position with used to hang up and glitch a lot.

Kasparov thought it strange that the program prevented the perpetual check threat in some earlier games of the match. But you can load this position into
a modern chess program and see that the difference in evaluation of the move
made by Deep Blue and the move that leads to the perpetual check makes around
0.1. This difference lies within the fluctuations caused by different program
settings – a slight change in weighting coefficients of positional factors can
be the reason here.

On the other hand, the perpetual check threat was more evident in the previous games – the position repeated three times much sooner. Modern programs
usually see this kind of repetition.

Now that we know that programs are prone to cut short all variants with big material losses, we may suppose that there are positions chess programs just
can’t evaluate correctly.

Here is an example (from grandmasters’ match):


The Black move.

I don’t claim a thorough analysis of this complex position, but it’s clear that the White must look for an escape, notwithstanding their extra pawn. The
Black move the rook to e4 and the White can’t take it because the Bc4 would be
taken in this case. Bc3 is responded by a sacrifice of the knight on g5, while
Nc6 can be answered by Qe8! The position is full of tactics. You might think
chess programs would feel at ease here. You would be wrong!

Fritz7, a recent world champion among chess programs, takes an hour on a 1.5GHz CPU to realize that the White are in no good
situation at all. And it takes the program several hours to evaluate the
position correctly. It just doesn’t see “silent” moves and so on.

As for another, more powerful programs, like Fritz8, they are not much stronger players than Fritz7. By the way, old ChessMaster 5000, a 1996-year
release, understands this position quicker than Fritz7. It has got a lot of
other problems, though.

Maybe more powerful processors would be of some help in these positions? Yes, this is possible. On the other hand, in the previous position the program
has to consider tens of moves. This would kill the performance growth
altogether.

Many amateur players like to put a game into the computer for analysis. But we should be careful about such evaluations. The program may say it’s only a
half a pawn advantage for an absolutely won position, or cry advantage in a
dead draw. Here, the high rating of a program shouldn’t deceive you: it doesn’t
necessarily mean the program understands positional playing correctly.

Having played for a while against chess programs, I came to my own recipe: try to make the best move possible in every situation. When you just make a
move that looks like good, without any plan in mind, it may bring you to
trouble against the computer.

Chess Programs Playing

So, chess programs are based on a simple and rather slow algorithm. How do they manage to beat human grandmasters? What are the tricks of a chess cheater?

A chess cheat has an openings note in his left pocket with trap variants marked red.

Yeah, every chess program has a huge openings library to consult with, while a man has none. I might divide the human memory into internal and external. A
paper sheet (a book) is a kind of CD, while human eyes are not far from CD-ROM
heads. Thus, a man is not only deprived of some part of his memory, but of a
specialized part. This is all right when humans compete with each other, but
not fair when two kinds of intelligence are involved.

There is an endgames book in the other pocket of the chess cheater.

Having found no other way to make the program good at endgame, program developers started feeding them databases of common endgames. Without this, the
program would be at a loss even in a simplest pawn endgame. That’s the reason
for chess programs being much larger today than before – they take whole CDs.
It turns out man plays against his own knowledge base
rather than against an AI. This is good for training young chess players, but
we can’t call it an achievement of AI.

Cheaters seldom work alone

The above-described matches were played between a man and a multi-processor machine. The processors were prompting to each other and exchanging ideas. This
doesn’t seem fair. You might go and load a huge chess program into all
Internet-connected computers and make it play against a single human. Wouldn’t
that be right? A man must play against his own desktop PC.

Cheaters catch the opponents

Cheaters like to let their opponent win, grow heated and then beat him. Chess programs also may be tweaked during a match. The technicians change
program settings and as a result create a completely different chess player. I
wonder if they would be happy to suddenly get Kramnik as an opponent instead of
Kasparov, with whom they were preparing to play. 

The chess cheater has another chessboard under the table to check a few variants.

Chess programs have a lot of memory at hand. It’s like they have a million of chessboards to make moves on. And the human has none. If I were Kasparov or
Kramnik, I would come to the match against the computer with my own board and
played all variants on it. The PC can’t see, you know. Who said you can’t touch
the pieces when playing against the machine? There is no such rule.

It’s like you play “blind” chess against a cheater. You are trying to figure it all out with closed eyes, while the cheater sees everything. 

Cheaters win from blunders.

All the games the computer won in the above-described matches were won due to blunders of the human opponents. They blundered
everything: a piece, a checkmate, a draw, an opening. The cheater can’t win
without that.

Conclusion

To good or bad, but artificial intelligence is still very far away from the human brain. The example of chess programs proves it.

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Man vs Machine - NY - Partie 6

mardi, 11 février 2003 • 15:50MarioSpina Commentez ici

Finalement, encore une partie nulle pour un match nul 3-3. Les joueurs se sont donc partagés 250 000$ USD chacun. On comprend que Kasparov n'a pas voulu prendre le risque de saboter un gain durant cette partie. J'imagine que si il avait eu une avance, il aurait vraiment risqué de finir la partie et arracher le gain. Comme il a dit lui-même: "Bien sûr que je voulait gagné, mais la priorité numéro un sur mon agenda aujourd'hui était de ne pas perdre." Conclusion: est ce que les machines nous auraient rejoint?

Voici un lien vers la partie: New York Man vs Machine, game VI

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Man vs Machine - NY - Partie 5

mercredi, 5 février 2003 • 23:07MarioSpina Commentez ici

La 5e partie du match et encore une nulle ! Elle fut aussi la plus courte partie du match avec seulement 19 coups. Au 10e coup, Deep Junior a joué un sacrifice de fou spectaculaire pour découvrir le Roi blanc et Kasparov très surpris dû travailler fort pour trouver les bons coups pour survivre à  l'attaque des noirs. On imagine facilement ce qui pouvait se passer dans la tête de Kasparov à  ce moment et les flashback qu'il a dû avoir en repensant à  ce que c'était passé en 1997 contre Deep Blue lorsqu'il avait été sévèrement puni après un sacrifice semblable. Encore une partie hautement intéressante et joué de main de maître par Deep Junior.

Voici un lien vers la partie: New York Man vs Machine, game V

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Man vs Machine - NY - Partie 4

lundi, 3 février 2003 • 22:51MarioSpina Commentez ici

Encore une nulle ! Kasparov a joué de façon beaucoup plus prudente et conservatrice espérant une erreur de son adversaire; Deep Junior a réussi à  arracher la nulle. Kasparov a trouvé le moyen de sortir des variantes connues dès le 8e coup sans se causer de problèmes. Par la suite, les joueurs se sont retrouvés dans une partie hautement positionnelle ou habituellement seul l'humain excelle. Encore une fois, les programmeurs de Deep Junior peuvent être fier de leur Å“uvre puisque Deep Junior s'est très bien débrouillé, bien qu'il est joué quelques coups inutiles entre le 9e et le 22e coup. Au 24e coup, Deep Junior a joué un coup que seul un humain (jusqu'à  ce jour) aurait pu jouer. Contrairement à  d'autre programme du même type, Deep Junior a su ne pas tenir compte de l'avantage matériel en sacrifiant un pion pour un avantage positionnel certain (un pion passé en b6), lui donnant des perspectives tactiques intéressantes.

Voici un lien vers la partie: New York Man vs Machine, game IV

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Man vs Machine - NY - Partie 3

dimanche, 2 février 2003 • 10:18MarioSpina Commentez ici

OUCH Bien que Kasparov ai dominé la partie au début, Deep Junior a su comment égaliser ses chances. Alors que Kasparov s'engageait vers la nulle, il n'a pas vu la variante qui assurait le gain d'un pion et de la partie par son opposant.

Le problème pour Kasparov fut de changer sa position gagnante en gain réel. En fait, dès le 21e coup, bien que Fritz 8 donne un avantage d’un demi pion au blanc, il est clair que c’est le type de position carrément impossible à  gagner contre un ordinateur. Il faut donc chercher la nulle à  tout prix et éviter au maximum les complications tactiques.

Encore une fois nous voyons pourquoi les machines nous battent : Elles ne dorment pas et ne font pas d’erreurs. Dans l’horizon de leur vision, elles voient tout.

Voici un lien vers la partie: New York Man vs Machine, game III

Kasparov's initiative slowly faded away against Junior's precise defense. Then he realized it was time to look for that draw, only he looked in the wrong place. 32.Ng6+ does not appear to be the forced draw the commentators believed it was, but it was definitely better than Kasparov's 32.Rh5??, which lost another pawn and the game to a brilliant tactical shot.

At the time we thought Kasparov was trying to play for a win. After the game he said he thought the rook move was the easiest way to force a draw. After the apparently forced 32...Qxd4 33.Rxh7+ Kxh7 34.Qxf5+ is a perpetual check draw. Unfortunately for humanity, in the diagram 32...Nxd4!! is a winner.

This seems impossible because of 33.Ng6+ Kg8 34.Ne7+ and it looks like black has to take a repetition. But 34...Kf8! and now if 35.Rxh7 Nb3+!! ouch, it's mate! This is what Kasparov missed with 10 minutes on his clock. 36.Kc2 (36.axb3 Qd1#) 36...Na1+ 37.Kc3 Qd2+ 38.Kc4 b5+ 39.Kc5 Qd6#)

Mig Greengard
- Chessbase.com

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Man vs Machine - NY - Partie 2

mardi, 28 janvier 2003 • 22:05MarioSpina Commentez ici

   Malgré une partie dominé par Kasparov (avec les noirs en plus); aujourd'hui, notre Super Grand-maître n'a été que trop humain. Il s'est laissé tenter par un échec au 25e coup alors qu'il avait un coup plus fort (25...f4) sécurisant le gain. Deep junior a su tirer son épingle du jeu en sacrifiant sa dame et en s'assurant une nulle par répétition.

   Nous voyons dans cette parti la grande force du jeu de Kasparov qui s'aventure dans une ligne non orthodoxe de la sicilienne avec confiance et réussi sans trop de mal a dominé le milieu de partie et à s'assurer un gain. Malheureusement pour lui, il n'est pas une machine fonctionnant a 100% de son efficacité en tout temps.

Voici un lien vers la partie: New York Man vs Machine, game II

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Man vs Machine - NY - Partie I

dimanche, 26 janvier 2003 • 23:29MarioSpina Commentez ici

Durant la première partie du match aujourd'hui, Kasparov a su démontrer la supériorité de son jeu et sa grande adaptabilité. Ce fut une partie enlevante malgré que l'on ai pensé que Deep Junior avait des ennuies techniques au 9e coup (vu le temps de réflexion qu'il a prit avant de jouer le coup).

Voici un lien vers la partie: New York Man vs Machine, game I

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Match - Kasparov vs Machine (Deep Junior)

lundi, 20 janvier 2003 • 23:09MarioSpina Commentez ici

Le match Kasparov vs Deep Junior commence ce weekend (le 26 janvier) à  New-York. Kasparov aura enfin la chance de se venger. Il sera possible de voir les parties en temps réel sur les sites de Chessbase et 3d World. Le match commence dimanche le 26 à  15h30 (heure de New-York). Les parties suivantes seront jouées le 28,30 janvier et le 2, 5 et 7 février; même heure même poste. Pour plus de détail, voici un article du NY Times paru aujourd'hui sur le sujet:

Chess Champion Faces Off With New Computer
By PAUL HOFFMAN

In 1809 in Vienna, Napoleon played chess at Schönbrunn Castle against the Turk, a turbaned mannequin that was heralded as the world's first chess-playing automaton.

Napoleon suspected that a human chess master was hidden inside, and he reportedly tried to interfere with the master's view of the board by wrapping a shawl around the Turk's head and torso.

But the blindfolded Turk still moved the chessmen quickly, in a jerky, mechanical fashion. Napoleon lost the game and angrily knocked the pieces to the floor. It took him several months to regain his concentration at the chessboard, and he continued to insist that the automaton was a fraud. (Indeed, it turned out to conceal a human.)

In 1997, Garry Kasparov, the Russian grandmaster who was then the world champion, played a highly publicized match, billed "as the last stand of the brain," against the I.B.M. supercomputer Deep Blue. The 1.4-ton refrigerator-size machine was a calculating monster. Its 418 processors routinely chewed through 200 million chess positions a second.

Mr. Kasparov lost the six-game encounter by a single game, and although he never swept the chessmen from the board, he did let his emotions get the better of him.

"The pressure got to me early," Mr. Kasparov recalled. "By the last game, I was in no condition to play chess or do much else."

Like Napoleon, he suspected foul play. He contended that Deep Blue might have cheated, an assertion that I.B.M. denied, by obtaining advice from human experts during the games. Mr. Kasparov demanded a rematch, but I.B.M. refused and mothballed the machine.

Pundits joked that Deep Blue had turned Deep Yellow.

Now almost six years later, Mr. Kasparov, who is 39, has found an appropriate silicon stand-in for the I.B.M. machine.

On Sunday, he begins a six-game $1 million match against an Israeli program, Deep Junior, the three-time world computer chess champion.

The match is sponsored by the World Chess Federation.

"I'm delighted," Mr. Kasparov said, "that the important scientific experiment of pitting man's imagination and creativity against a machine's calculating power can continue under fair conditions."

The pioneers of computing thought that creating a machine that played top-flight chess would be simple. They regarded chess as a game of calculation, and calculation, of course, is what computers are good at.

In 1957, the mathematical economist and Nobel laureate Herbert A. Simon predicted that a machine would become the world chess champion in a decade, and he was just the first in a long line of illustrious scientists whose forecasts about computer chess were notoriously wrong.

Cognitive psychologists discovered that grandmaster chess was more of a game of pattern recognition than calculation. But no programmer succeeded in codifying that more elusive ability into a set of rules that a machine could follow.

The situation today is that both humans and machines can play world-class chess, but they approach the game completely differently.

Deep Junior typically examines three million positions per second. But even at that speed, it cannot generally see that far ahead.

That is because the number of possible chess positions is staggering. There are some 85 billion ways of playing just the first four moves for each side.

The strength of world-class human players lies in their ability to decide who stands better in a given position, and that ability — general chess knowledge, if you will — is hard to build into a machine.

Mr. Kasparov concedes that he examines only one to three moves a second, but he suspects that they are the strongest ones.

Humans are best at long-range strategic planning, where subtle, methodically executed maneuvers ultimately carry the day. Deep Junior excels in hand-to-hand combat, tactical dogfights in which brute computational strength prevails.

"In the upcoming match, the contrasting styles promise exciting games," said Bruce Pandolfini, a chess master in New York.

The match will be played at the New York Athletic Club, and the games will be shown in real time on the Web at www.x3dworld.com and www.chessbase.com.

The play will start at 3:30 p.m. on Jan. 26, 28, and 30, and on Feb. 2, 5, and 7. Each game will last at most seven hours.

"Garry is the greatest human player ever," Mr. Pandolfini said. "He wants to restore man's supremacy in chess by thrashing the computer. And he wants to upstage Vladimir."

He was referring to Vladimir Kramnik, 27, his countryman and a former protégé who ended Mr. Kasparov's 15-year reign as world champion in 2000.

Even though Mr. Kramnik now has the world crown, Mr. Kasparov is still ranked No. 1 on the chess federation rating list. He will not have a shot at winning the title back until November, at the earliest.

"In the meantime, Garry will have to be content with trying to better Vladimir's accomplishments," said Alexander Baburin, editor of a daily Internet publication, Chess Today, at www.chesstoday.net/.

Three months ago, Mr. Kramnik split six games in a $1 million match of his own against a cousin of Deep Junior, Deep Fritz, a German program.

The match rules were drawn up to eliminate some of the perceived inequities of Mr. Kasparov's struggle with Deep Blue. Mr. Kasparov went into that contest without ever having seen a game played by his opponent (although he himself had beaten a slower and weaker incarnation of the computer in 1996). Deep Blue, on the other hand, was able to analyze hundreds of Mr. Kasparov's games.

Before Mr. Kramnik's match, Deep Fritz's handlers had to provide the world champion with a copy of the software and promise not to change it later. Experts say that requirement put the machine at a disadvantage. Human players, after all, are free to adjust their playing style any time they want.

"It was a terrible thing," said Frederic Friedel, a co-founder of ChessBase in Hamburg, the manufacturer of Deep Fritz. "Kramnik had Fritz's brain in a bottle. He could figure out what Fritz would play in a given position."

Even so, although Mr. Kramnik won two games from the computer, he was unable to win the match.

The organizers of Mr. Kasparov's contest with Deep Junior are trying to strike a balance between the computer-friendly conditions of the Deep Blue competition and the pro-human rules of the Deep Fritz match.

"I've received a relatively fresh copy of the software," Mr. Kasparov said. "Although the programmers are allowed to tinker with it all they want, except when we are actually playing."

Who is favored to win? Mr. Friedel thinks that Mr. Kasparov will triumph as long as he keeps his cockiness in check.

"Junior is a street brawler," Mr. Friedel said. "You remember `West Side Story'? It's the Jets. It will be constantly taunting Garry. `Do you want to fight with knives? Whips? Pistols? Machine guns? You choose the weapon.' If he knows what's best for him, he'll say, `Let's stay in the ring and keep these big soft gloves on.'

"But it's not his nature to duck a challenge. My advice to Garry is to stay focused and get a lot of rest. The machine will never tire or fret over a loss."

One issue that the match will not resolve is whether Deep Junior plays better chess than Deep Blue.

"Deep Blue's victory over Kasparov was a milestone in artificial intelligence," Mr. Friedel said. "But it's a crime that I.B.M. didn't let it play again. It's like going to the moon and returning home without looking around."

Although Deep Junior, which is planned to run on eight processors, is much slower than the I.B.M. computer, experts said it had more chess knowledge built into it. It apparently considers more elements in judging a position.

"Junior is very humanlike," Mr. Kasparov said. "It's a computer version of me. It plays forcefully, imaginatively and takes risks."

Deep Junior is a program, rather than a custom-made chess-playing machine like Deep Blue. A single-processor version of it can be purchased for less than $50 and run on a PC.

"The match will be close, but I'm determined to win," Mr. Kasparov said. "One thing I know is that humans' days at the top of the chess world are limited. I give us just a few years."

"The only sure way to defeat a computer," Mr. Baburin said, "will be to cut its power source."

via: New York Times Online

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Man vs Machine, Final

samedi, 19 octobre 2002 • 15:07MarioSpina Commentez ici

kramnik-game8.jpg Après la 7e partie où Fritz n'a pas réussi à  avoir de gain, il obtient la nulle en 8e partie avec les noirs. Le résultat est donc égal (4 - 4) et ennuyant. Nous aurions bien aimé voir un joueur se démarquer, mais comme c'est souvent le cas à  ce niveau de jeu, les joueurs ne font aucune concession et préfèrent annuler plutôt que prendre des risques (d’ailleurs on a vu ce que prendre des risques donnait en partie 6).

Étrangement, ce match me laisse perplexe... est-ce que le résultat aurais été prévu d’avance? L’équipe Chessbase (les concepteurs de Fritz) aurait perdu une certaine crédibilité si elle avait été défaite. Il ne fait aucun doute non plus que Kramnik préférait ne pas perdre la face. Avec les retombés publicitaires importantes du match, que doit-on penser?
Vous pouvez rejouer les parties 7 et 8.

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Man vs Machine, partie 6

mercredi, 16 octobre 2002 • 08:00MarioSpina Commentez ici

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Deep Fritz gagne la 6e partie et égalise ! Je dois vous avouer que je suis surpris. Kramnik a tenté de battre Fritz sur son propre terrain en tentant une envolé tactique. Il est très dangereux de jouer de façon agressive contre la machine dans une position le moindrement complexe, celle-ci se défendra de façon parfaite et il y a de fort risques que l'attaque s'essouffle. Il semble bien que ce soit ce qui est arrivé à  M. Kramnik après son sacrifice en f7. Kramnik dit qu'il ne pouvait résister à  la chance de jouer la plus belle partie de sa vie. Malheureusement, Fritz n'est pas humain et il n'y a aucune façon d'influencer son moral. Je me rend bien compte que si Kramnik aurait fait face a une joueur humain, il aurait eu beaucoup plus de possibilité de gain puisque le psychologique entre en jeu. Vous conviendrez que jouer les noirs dans cette partie aurait été décourageant. Vous pouvez voir la partie ici.

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Man vs Machine, partie 5

lundi, 14 octobre 2002 • 13:00MarioSpina Commentez ici

Paf La machine infernale réussie à  garder les dames en jeu et tabasse Kramnik. Il faut dire que celui-ci était en pression de temps avec 15 min pour jouer les 6 prochains coups avant le contrôle de temps. Il a fait une gaffe et perd une pièce. Il abandonne le coup d'après. À voir !

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Man vs Machine, partie 4

samedi, 12 octobre 2002 • 09:00MarioSpina Commentez ici

Fritz arrache la nulle à  Kramnik (2 - 1 pour Kramnik) ! Tarrasch Defence, une fois encore, Kramnik réussi à  éliminer les Dames du jeu, ce qui réduit son risque. . Kramnik garde la paire de fou... le jeu est plus dangereux pour Fritz, mais Fritz garde une structure imprenable et parvient à  égaliser.

Vous pouvez consulter la partie ici.

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Man vs Machine, partie 3

mercredi, 9 octobre 2002 • 07:30MarioSpina 6 Commentaires

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Ouch ! La machine en prend encore pour son rhume. Kramnik a démontré une très bonne connaissance des faiblesses de la machine pour la planification à  long terme en fin de partie. Bien qu'il ait atteint une position confortable en milieu de partie (après une Scotch), il semble que Fritz n'avait aucune idée de la façon de continuer par la suite. Kramnik avait bien choisi le lieu et le moment de la bataille, un milieu de partie sans Reine avec une structure de pion rigide qu'il pouvait changé à  sa guise. Résultat: Kramnik 2.5 - Fritz 0.5

Personnellement, je pense qu'on est loin du Deep Blue d'IBM. Il semble que Deep Fritz ne soit pas à  la hauteur. Mais gardons nous de faire des pronostics, on ne sait jamais.

Vous pouvez rejouer la partie ici.

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Man vs Machine, partie 2

lundi, 7 octobre 2002 • 10:14MarioSpina Commentez ici

Le choc des Titans part II... Kramnik gagne (1.5 à  0.5 pour l'humanité). Il semble que les membres de l'équipe de Fritz ont été un peu embarassés par le 12e coup (Bf8) de celui-ci. Un coup totalement bizarre et inhumain pondu par une machine. Il semble que Fritz évaluait que Kramnik allait retraité avec son cavalier, au quel cas, Fritz aurait répété le coup de fou, tentant la nulle. Naturellement, Kramnik n'ayant pas l'intention d'annuler, il en a profité. ICI pour rejouer la partie commentée.

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Man vs Machine, partie 1

vendredi, 4 octobre 2002 • 11:09MarioSpina Commentez ici

kramnik08e.jpg

La partie Deep Fritz - Kramnik vient tout juste de Se terminer. Kramnik avait les noirs et il est parvenu à  annuler. Je vous rappelle que la cadence de jeu est lente (premier contrôle à  2h/40 coups). Si il faut croire les commentaires durant la partie, ce fut une nulle difficile à  obtenir par Kramnik puisque les blancs avait un certain avantage. Plus de détails là -dessus plus tard, après analyse.

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Chips vs. the chess masters

mardi, 1 octobre 2002 • 07:25MarioSpina Commentez ici

Un autre article intéressant sur le combat de titans qui aura lieu sous peu entre Kasparov - Deep Junior (décembre) et Kramnik - Deep Fritz (octobre). On y fait l'apologie de Deep Blue en expliquant les différences de design entre les 3 systèmes. En gros, il semble que l'exercice soit beaucoup plus une campagne publicitaire pour les créateurs des logiciels Deep Fritz et Deep Junior qu'un réel match. Il semble clair pour tous (excepté les concepteurs des logiciels) que Deep Blue était largement supérieur en force brute comparativement à  ces derniers. Naturellement, il sera très intéressant de voir la réaction des gens si un des deux humains perd contre une machine probablement moins forte que Deep Blue.

links: Brains In Bahrain

Five years after a historic defeat, humans may be poised for a comeback. When IBM's Deep Blue supercomputer beat chess champion Garry Kasparov five years ago, the case seemed closed: The wetware of the human brain was simply no longer a match for the hardware of a chess-playing machine. So what, exactly, is the point of Man vs. Machine, Round II–two upcoming contests that pit today's best chess programs against Kasparov and the current world champion, Vladimir Kramnik?


Deep Blue's 1997 victory over Kasparov felt hollow to computer chess experts. They had dreamed for decades about beating the world's best human, but in the event, Kasparov fell apart psychologically. He later said he felt "ashamed" of the way he'd played, having made an obvious blunder in the final, deciding game. "I should have been exulting, but I was feeling empty inside," writes Deep Blue team member Feng-Hsiung Hsu in Behind Deep Blue, a memoir published this fall. "The game felt too easy."

As a result, Kramnik says, no one really knows how computer chess measures up. "The question was still open, is still open: Is it stronger than, let's say, the strongest human being?" Deep Blue isn't available for a rematch, so these new contests aim to answer that question. But this time it could be the computers that aren't at the top of their game. Some experts say that, compared with Deep Blue, they may actually be a step backward...

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Echec et Mat

mardi, 3 septembre 2002 • 14:43MarioSpina Commentez ici

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Ma participation au tournoi Ouvert de Montréal, bien que passablement excitante, n'auras pas été très fructueuse. Je n'ai récolté qu'un demi point sur les 4 parties que j'ai jouées. Il faut dire que l'appariement (la méthode pour créer les "paires" de joueurs pour les parties) était mal foutu. Les arbitres ont décidé d'utiliser un appariement dégressif avec une seule classe (habituellement, les joueurs sont appariés avec d'autres joueurs de leur classe). Certains joueurs se sont donc retrouvés à  jouer contre des joueurs ayant plus de 500 points de cote de différence. J'ai donc joué ma 3e partie contre un Maître ayant plus de 1200 point de cote de plus que moi. Ce fut une partie fort intéressante, mais éprouvante. J'aime bien joué contre des joueurs plus forts que moi, mais il y a une certaine limite.

Si vous êtes intéressé de voir mes modestes parties, vous pouvez les trouver ici.

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Kasparov veut sa revanche !

mardi, 13 août 2002 • 10:21MarioSpina Commentez ici

Garry Kasparov à  annoncer qu'il tenterait de prendre sa revanche contre les machines en Octobre à  Jérusalem. Il jouera contre Deep Junior pour une bourse de 1 millions de dollards US ! Le match se tiendra en même temps que celui jouer par Kramnik contre deep Fritz.

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La plus ancienne pièce de jeu d'échecs en Europe

jeudi, 1 août 2002 • 11:50MarioSpina Commentez ici

Pour ceux que l'archéologie intéresse, voici un petit article intéressant: Libération : Découverte de la plus ancienne pièce de jeu d'échecs en Europe

Des archéologues ont mis au jour en Albanie ce qui semble être la plus vieille pièce de jeu d'échecs jamais retrouvée en Europe. Cette découverte donne à  penser que les échiquiers étaient déjà  en vogue un demi-millénaire au moins avant le XIIe siècle, considéré jusqu'à  présent comme la période d'apparition du jeu sur le Vieux Continent.

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