How to Beat Chess Computers

“Chess computers have no fear” – Yasser Sierawan, Chess Grandmaster, describes one competitive advantage of chess computers.

Do Humans have a Chance to Beat a Chess Computer?!

This article goal is to assist you winning a bit more often against chess computers. A few things that really help are to be able to understand how they work and think, be able to predict what sorts of things they might not spot and to understand what parts of the game a computer is better than humans at playing.

Since Deep Blue victory over Gary Kasparov it has become apparent to all that computers are very good at playing chess. But, fortunately, they still have weaknesses, so with a little preparation the results of playing against chess machine can be improved. A chess computer assesses who is ahead in a slightly different way to how many human players would. The piece values are the same, but not all humans would consider some of the other factors that a computer does.

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How chess computers play

There is a simple way to play perfect chess: write down all the possible games of chess, note if the final position is won, drawn or lost, and then working backwards assume each player chooses the best line, you will eventually end up with a list of all the best possible games of chess. Even for the strongest computers nowadays, this approach is clearly impractical. A compromise is to grow the list of variations as large as possible, in the time permitted, and then use an evaluation function to try and decide the likely outcome from the final position of each variation.

Computers use evaluation functions to determine as accurately as possible the likely outcome of the game from that position, however it doesn’t need to take account of a great deal of tactical information, as hopefully the tactics in the position are taken care of in the variations themselves. In this sense the evaluation function contains the strategic knowledge the computer has of chess. The evaluation function of commercial chess programs is generally a closely guarded secret, but generally speaking it takes into consideration the following factors:

  • Material
  • Trapped Bishops
  • Development
  • King Safety
  • Trapped Rooks
  • Weak Back Rank
  • Knight/Bishop Outposts
  • Centralization
  • Bishop Pair
  • Pawns on same color as a single Bishop
  • Rooks on Open Files
  • Doubled Rooks
  • Rooks behind passed pawns
  • Rooks on the 7th Rank
  • Bishops of Opposite Color
  • Developing minor pieces before the Queen
  • Various end games related advantages – such as avoiding having the wrong rooks pawn in a King and Bishop ending.
  • Passed Pawns
  • Isolated and Backward pawns
  • Doubled Pawns

The computer subtracts your score from its own score. A positive score means the computer is ahead and a negative score means that the human is ahead.

How chess computers calculate variations

As you can see the computer may be doing a lot of work in evaluating a single position. Yet, clearly some more sophistication is needed in the evaluation function to play sensible chess. This means it has less time for calculating the possible variations, and so a more complex evaluation function may result in loss of tactical ability. Chess positions have a lot of variations, and the more possible positions we come up with the more time must be spent evaluating them. It is important to identify quickly when a particular variation is not worth following up. Mostly the computers use techniques based on ordering moves, so they try the most promising first. Having identified lines that are not promising, it will not evaluate these lines as fully to save time. This means it may not consider all possible sacrifices very deeply.

Effective pruning is vital to ensuring we consider only the most appropriate moves. Humans are thought to be particularly good at recognizing the significant moves in a position – it is believed that Grand masters only consider an average of 1.7 moves in any position (obviously recaptures keep the average down).

Similarly we must ask how big to grow the tree – well obviously the time constraints of tournament play restrict our choice – but also we don’t want to stop in the middle of a sequence of captures and recaptures – as the evaluation function might wrongly conclude we are a piece up or down incorrectly.

So a lot of work in computer chess goes on ensuring that the tree of variations is grown appropriately, and that branches that are tactically interesting receive more attention.

Strengths and Weaknesses in Computer Play


Tactics – modern computer programs are stunning tactical players.

Openings – Most modern chess programs have an extensive opening book, and so play according to the best theory in many lines. This is a strength – wouldn’t we all play better with a copy of ECO – although sometimes computers find themselves a little lost at the end of their opening book when first forced to think for themselves.

Standard Endgames – are a strength as modern programs have databases, computers are appalling at endgames. Probably because tactics are if not less important, then easier to see, whilst ideas become more important.


Positions understanding – computers don’t understand even simple positions they just evaluate. If something will not happen they can not understand that.

Non-Standard Endgames – though, computer rely on endgame pre-defined evaluation based on material left, rather on position nuances. For example, see an opposite colored Bishop ending features in the Kasparov – Deep Blue 1996 match, round 2 game given later – the chess computer concluded that the opposite color Bishops lend the position a more end game nature, while in the particular position, the opposite colored Bishops played an important attacking feature rather than a drawing factor. There are many examples of chess computers loose simpler endgames (many from ‘lost’ positions) – although for the less strong player the endgame weakness of computers might be harder to exploit: you have to know how to play endgames well…

Special positions – Zugzwang, for example, can be difficult for computers. The pieces are on okay squares, there are perhaps a few pawn moves available before one side is forced to make a worsening piece move, so the computer can not see that the forthcoming doom.

Losing combinations and there effect on Computers (How to Win “Won” games)

The ‘Horizon’ effect is what happens when you don’t calculate variations deep enough to understand the tactics. Worse still if you see something bad is going to happen (losing a piece) and you can put it off for a few moves by say sacrificing a pawn, then the machine may not realize it is still going to lose a piece. Thus it sees a choice between losing a pawn and losing a piece, and chooses loosing the pawn – eventually it loses both a piece and a pawn. The horizon effect is often noticeable when the computer’s opponent has mating possibilities – anything to avoid the loss of the King. Similarly when the loss of the King is unavoidable the computer may turn suddenly materialistic – ignoring the mating threats whilst grabbing material so as to lose by the smallest margin. Both these situations may lead to unnatural moves – since the computer is frequently a better tactician than the human – it may start making these unnatural moves before you realize you have a win.

Strategies for Beating Computers

Breaking rules of Thumb – The chess computers evaluation function is basically a list of ‘rules of thumb’ and when you give it a position where these rules aren’t valid it will still blindly follow them (unless it can see the consequences).

Opening choices against a master Tactician – When faced with a master tactician one should try to avoid open positions, where simplistic strategies of centralization, and tactical awareness predominate. Openings that tend to lead to ‘quiet’ positions are to be preferred against computers. For example, in positions with a closed center and a build of wing attack behind a pawn advance, in most cases modern computer programs seek counter play with a pawn advance on the opposite wing, but they are outplayed as they don’t recognize patterns for opening files, and make lacklustre moves. Opening choices against a

Bookish Opponent – As we have said modern computer chess programs have extensive opening books, but often primitive understanding of a position, so one strategy that has been used successfully is to deviate from book early. Many of the Grand masters do not use this strategy – perhaps they have the advantage of being familiar with all the latest analysis – possibly even more up to date than the machines – but for the less strong players, this is a useful strategy and probably will give the club player good practice at thinking through opening plans.

Win the Endgame – look for non-standard Endgames, in which chess computers can be mislead by the pre-defined evaluation of position.

Play Great Chess – always a good strategy in chess and can make up for not following any of the above advices…

The Kasparov-Deep Blue matches

In 1996, the World Chess Champion of that time, and believed to be the strongest chess player ever, Garry Kasparov, meet in a match Deep Blue chess-playing computer, developed by IBM. Kasparov beat the Deep Blue by a score of 4?2, yet the chess computer made an history: Deep Blue became the first machine to win a chess game against a reigning world champion under regular time controls.

Deep Blue was then heavily upgraded and played Kasparov again in May 1997, winning the six-game rematch 3??2?, becoming the first computer system to defeat a reigning world champion in a match under standard chess tournament time controls. When Kasparov requested that he be allowed to study other games that Deep Blue had played so as to better understand his opponent, IBM refused. After the loss, Kasparov said that he sometimes saw deep intelligence and creativity in the machine’s moves, suggesting that during the second game, human chess players had intervened on behalf of the machine, which would be a violation of the rules. IBM denied it, saying the rules provided for the developers to modify the program between games, an opportunity they said they used to shore up weaknesses in the computer’s play that were revealed during the course of the match. Kasparov accused IBM of cheating and demanded a rematch, but IBM refused and dismantled Deep Blue. You can view below are the tvelve games played in these two matches.