Computers and human beings play chess differently. Thebasic paradigm that computer programs employ is known as“search and evaluate.” Their static evaluation is arguably moreprimitive than the perceptual one of humans. Yet the intelli-gence emerging from them is phenomenal. A human spectatorcannot tell the difference between a brilliant computer gameand one played by Kasparov. Chess played by today’s machineslooks extraordinary, full of imagination and creativity. Such ele-ments may be the reason that computers are superior to humansin the sport of kings, at least for the moment.

Not surprisingly, the superiority of computers over humans inchess provokes antagonism. The frustrated critics often revert tothe analogy of a competition between a racing car and a humanbeing, which by now has become a cliché.

In 2007, Amir Ban (my partner) and I flew to Elista, capital ofKalmkya, to take part in a chess match dubbed “The UltimateComputer Chess Challenge.” Our program, Deep Junior, thenthe world computer chess champion, was to play a matchagainst Deep Fritz, which a few months earlier had defeatedVladimir Kramnik, the world’s human chess champion.

Parallel to the computers’ match, at the same venue, some ofthe best human chess players assembled to play in the semifinaltournament for the world title. FIDE, the international organiz-ing body, had asked top commentators to annotate the gamesin both competitions. Among the commentators was BorisSpasky, the famous former world champion.

Some of the games between Deep Junior and Deep Fritz wereexciting: in one of them, Deep Junior was able to discover animportant theoretical novelty in the Sicilian defense. After sac-rificing no fewer than four pawns, it managed to find a winning

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Deus Ex Machina—A Higher Creative Species

in the Game of Chess

Shay Bushinsky

n Computers and human beings play chessdifferently. The basic paradigm that computerprograms employ is known as “search and eval-uate.” Their static evaluation is arguably moreprimitive than the perceptual one of humans.Yet the intelligence emerging from them is phe-nomenal. A human spectator is not able to tellthe difference between a brilliant computergame and one played by Kasparov. Chessplayed by today’s machines looks extraordinary,full of imagination and creativity. Such ele-ments may be the reason that computers aresuperior to humans in the sport of kings, atleast for the moment. This article is about howroles have changed: humans play chess likemachines, and machines play chess the wayhumans used to play.

maneuver that was not known beforehand. Disap-pointingly, the commentators shied away from thecomputer games, refraining to analyze them.

Mig Greengard, an influential chess commenta-tor, tried to explain why humans avoid computergames. According to him, spectators prefer towatch how chess icons lose by committing terribleblunders of the kind that do not occur any more incomputer chess. Perhaps this provides them withconsolation for their own imperfections.

Perhaps Greengard’s comment agrees withGrandmaster Robert Huebner’s view that “we playchess because we do not know how to play it”; inother words, mistakes are just part of the fun ofplaying. However by ignoring computer games,humans miss the great progress that has beenmade and the beauty of their play—just as if onechose to ignore a beautiful picture merely becauseit was painted by a computer.

Some of the people who take an interest in com-puter chess admit computer superiority but castdoubt over the methods employed to achieve it.They revive the term brute force in reference to theoverwhelming computer search mechanism,pointing out that mortal players reach comparableresults looking at many fewer positions. Whileindeed human abilities really are amazing, beneaththeir hood hide millions of neurons probably per-forming primitive calculations as well. To quote

Marvin Minsky, “We are not just simple machines,we are wonderful ones.”

This article is about how roles have changed:humans play chess like machines, and machinesplay chess the way humans used to play.

Computer Intuition and Concrete Play

It is conceivable that the computer’s “chess neu-ron” (a role attributed to its static evaluation func-tion) is superior to the human one because it isdesigned to perform a limited yet special task.Either way, the net result in both cases is the emer-gence of a creative thinking process. The spectatorwitnesses the beautiful expression of the machine’sinner self and should not be bothered by all thoseneurons. In this sense, computer chess programspass the Turing test big time.

Another interesting analogy exists in a compar-ison between the intelligent search process thatoccurs in clever computer chess machines  andswarm intelligence (SI). SI imitates animals that col-laboratively perform smart tasks and are veryrobust even though each individual is stupid andweak (for example, ant colonies and bees). Someconsider the hive itself as the actual organism andnot each component individual (Kelly 1995).

The fundamental differences between the waysboth species play the game are reflected in theirstyle. The human style can be characterized as log-ical. Human players tend to develop their gameaccording to a plan. When humans are taughtchess, they are often warned to stick to a plan at allcosts. “A bad plan is better than none” goes thesaying. Accordingly, when reasoning why playerslose, a popular explanation is the choice of thewrong plan.

Computer programs do not play according toplans. In fact, attempts to design effective chessprograms to this end failed a long time ago. Theyrather “play the position.” This is why until thelate 1980s; programs were ridiculed for not know-ing what they were doing. However, in our era, thehuman experts characterize the computers’ way ofplaying as of “concrete style.” Grandmaster RamSoffer believes that Garry Kasparov may be consid-ered the father of the computer style since he pio-neered the age of concreteness. His successors,which are computers, have dramatically influ-enced the way chess is being played today. Kas-parov was not involved in designing computersoftware. The linkage is probably due to the rightway chess should be played.

“Playing the position” means that computers areoften much more objective in their assessmentthan humans. It also makes computers more flexi-ble and thus more creative across the board. Thecomputers’ big leap during the past decade was in

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© Angelo Marcantonio

understanding the concept of positional compen-sation. By the end of the 1980s, the common feel-ing among experts was that computers would sac-rifice material only for very concrete reasons.When humans sacrificed for a long-run positionaladvantage without an obvious gain, they werebelieved to be highly inspired, with stroke-of-genius category intuition. The concept of a “posi-tional sacrifice,” for which there is no immediategain within the searchable horizon, seemed unat-tainable by AI and solely God’s gift to man. Com-puter programs’ weakness was ridiculed because oftheir innate greed.

When Deep Blue sacrificed its white knight inthe French opening against Garry Kasparov duringtheir final decisive game (New York, 1997), Kas-parov felt cheated. Deep Blue did not make the sac-rifice on its own. Rather it followed a human moveplayed before. Perhaps Garry forgot about thatmove himself, or was preconditioned to the ideathat computers cannot make positional sacrifices.The move was actually entered into the program’sopening book by Grandmaster Joel Benjamin.Therefore, Deep Blue played it automatically with-out wasting a single CPU cycle over it. In retro-spect, without the book, it is unlikely that DeepBlue was capable of playing that move on its own.

Six years later, when Deep Junior sacrificed itsblack bishop in the Nimzo-Indian defense of itsfifth game against Garry Kasparov (New York,2003), it made our human opening expert think ithad gone berserk. This time, the sacrifice was madepurely out of Deep Junior’s “imagination” and wasnot motivated by a concrete materialistic gain. Inthe postmortem, Kasparov remarked that progressin AI has been achieved.

The move depicted in figure 1 is an imaginativesacrifice, surprising, to say the least. Such early-stagesacrifices were popular during the romantic periodof chess (1850–1920). This move is not a concretecombination aimed at gaining some material laterfor the bishop. It is more like Deep Junior saying,“Let’s throw away this piece and flush out Kasparov’sking—perhaps this will work out OK….”

Kasparov raised his eyebrows at this move, buttook the bishop without much thought. He playedKg3 derisively, looking left and right, as if asking,“Is this serious—are you trying to tease the worldchampion with stupid moves?” The pattern of thissacrifice and what happens next is well known.With black’s pieces undeveloped and no other sup-porting feature in black’s position, what is there toconsider? Moreover, how can it succeed? These aregood questions, but not the kind that Deep Juniorasks itself: from here on all evaluations were 0.00.

The two senior grandmaster commentators—famous grandmasters Yasser Seirawan and MauriceAshley—remarked instantly that Deep Junior wasabout to lose quickly here. The general comment

was “hmmm … interesting—but Deep Junior isgoing to lose in a few moves here.”

It turned out that Deep Junior understood moreabout the position, and after a long think by Kas-parov, the game ended in a draw. A few days later,in his concluding remarks about the match withDeep Junior, Kasparov admitted that the sacrificewas sound.

Tracing the Sources of Human Creativity in Chess

The game of chess has exact rules but inexact prin-ciples for playing it. Many of these principles werediscovered over centuries through insight andexperience and are documented in chess literature.Careful authors mention the fact that for almostany given principle there is an exception that mayoccur in certain positions. Some of these principleseven conflict with each other. Knowing how toplay chess well is largely about applying good judg-ment as to when a particular principle should beapplied and when it should be broken.

Famous past world champions such as MikhailTal and Garry Kasparov are often singled out asexceptionally creative. So are Alexei Shirov and

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Figure 1. Garry Kasparov Versus Deep Junior 8X3D Man-Machine Match, New York, 2 May 2003.

Notes by Amir Ban: 1.d4 Nf6 2.c4 e6 3.Nc3 Bb4 4.e3 0-0 5.Bd3 d56.cxd5 exd5 7.Nge2 Re8 8.0-0 Bd6 9.a3 We are out of book here 9 ...c6 10.Qc2 Bxh2+

Alexander Morozevich, who are active top grand-masters. Grandmasters examining their games cor-relate their creativity with a high flexibility inbreaking the principles.

The elements that spark creativity in chess areno different than in other domains.

Creativity in chess could be explained using theEduard De Bono theory of lateral thinking. Accord-ing to De Bono (1970), the creative process is high-ly motivated by techniques such as (1) noncon-formism; (2) provocation (in the positive sense);(3) flexibility; (4) casting doubt; (5) “thinking outof the box”; and (6) transfer.

The following example, given by GrandmasterBoris Alterman, might provide a clue: Suppose thewhite queen is attacked by a black piece. Ninetypercent of the players will instinctively move thequeen to evade the threat. Nine percent wouldconsider an option to counter-threaten the blackqueen. Only 1 percent would seek anotheroption—perhaps an effective intermediate movethat could be made.

It is perhaps paradoxical that there is a strongconsensus amongst chess experts that human cre-ativity in chess stems from a traceable orderlyprocess. Amazia Avni, a psychologist and a chessmaster, analyzed the roots of creativity in humanplay in his book Creative Chess (Avni 1998). Hedescribes an intelligent process composed of fourdistinct steps: (1) gathering—collecting the rawmaterials during position evaluation; (2) synthe-sis—opinion forming and plan shaping; (3)enlightenment—a sudden observation of an idea;(4) realization—translating the idea into practicallines of play. Again, not unexpectedly, this mightbe a good recipe for a creative process that couldpossibly work in other domains.

Avni goes further to outline particular creativeelements in the game of chess that stem creativity,among which he mentions nonstandard position-ing or functions of chess pieces, the removal ofone’s own piece, the breaching of theoretical prin-ciples, observation of small nuances occurring in aposition, and many more.

Grandmaster Alon Greenfeld, Deep Junior’sopenings consultant, is a strong believer ofmethodical creativity. He explains that often heinstructs his students not to despair upon findinga hole in a good idea. “For example if there is a tac-tic that doesn’t work on the f7 square, try the movecombination in a different order. Surprisingly youmay discover a different way to implement theidea,” Greenfeld explains.

In parallel to his early stages in chess, Grand-master Greenfeld took an interest in military his-tory. He recalled having borrowed the idea ofavoiding piece exchanges when holding a spaceadvantage directly from military strategy.

World champion Tal once told how his chess

game was inspired from watching ice hockey! Henoticed that many of the hockey players had thehabit of hanging around the goal even when thepuck was far away from it. He realized that thishabit helped them score when an opportunitycame. Following the hockey game, he decided totry transferring the idea to his chess game by posi-tioning his pieces around the enemy king, still pos-ing no concrete threats. Indeed, in a later stage, anopportunity arrived to use them to checkmate hisopponent’s king.

It turns out that many of the creative processestake place off the board during players’ preparationand analysis. During the game, subjective circum-stances such as psychological blocks impair thecreative process. Stress and external factors such asthe current standing in the tournament and adesired result restrict a player’s tendency to take“unnecessary risks.” What might be consideredrational behavior constrains creativity.

As a direct result, counter-creative chess is oftendemonstrated. Take the recent “grand slam” Wijkaan Zee tournament as an example. While I waswriting this article, more than two-thirds of thegames ended in a draw. The chess played in manytournaments is often very conservative—expertsfollowing the games mark over 90 percent of themoves played as highly predictable.

A common speculation attributes the “drawingphenomenon” to very high-quality chess. Howev-er, in the 2008 world computer chess champi-onships held in Beijing, only 19 percent of thegames ended in a draw, most of which were ofabsolute high quality. The same trend continuedfor the 2009 world computer chess championshipsheld in Pamplona where 27 percent of the gamesresulted in a draw while in parallel; a Super GMtournament was held in Sofia with 63 percent ofthe games resulting in a draw. 

Computer Resources of CreativityIt might cause some despair among human playersto realize that some of the most ingenious chessmaneuvers are discovered by programs like DeepJunior in less than a second. Equally annoying per-haps is Deep Junior’s ability to discover “holes” insome of the most brilliant pieces of chess gamesplayed, ruining pieces of art.

It is important to note that strong chess pro-grams are not developed for this purpose. Nor doesthe solving of a particular chess problem serve as aguideline for progress. In fact, stronger programversions are often less successful in solving suchproblems. Their mere capability is another indica-tion of the “emerging intelligence phenomenon.”The good news is that neither Deep Junior nor itssiblings can find all the creative moves ever playedby human players; however the bad news is that

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the number of positions beyond computer reach isdiminishing rapidly.

The human expert’s observation that creativity inchess is a methodical process strengthens the opin-ion that machines can indeed be creative. There-fore, it is an interesting exercise to identify analo-gies between human creational devices andcomputer chess ones. Grandmasters emphasize thatthe inventive processes must be efficient. Makingchess programs efficient is always a good invest-ment. It is common knowledge that chess programsrely heavily on heuristics. Inherently, heuristics aremethods that help programs in problem solving, inturn leading to learning and discovery.

Artificial Intelligence theory encourages the dis-covery of effective heuristics by neglecting logicalconstraints in the problem. This process is com-pletely analogous to Genrich Althuller’s TRIZmethod for sparking creativity (Altshuller 1973,1984). TRIZ is based on the following principle:“Inventing is the removal of technical contradic-tion with the help of certain principles.”

Deep Junior’s evaluation function implementssuch heuristics in many different ways. One exam-ple is a code allowing it to evaluate hypotheticalpositions for pieces by moving them to squareseven beyond pieces blocking them. This heuristic,which simulates illegal moves, is completely anal-ogous to “contradiction removal.” As a matter offact, instances of this heuristic are heavilyemployed in single-agent search as well.

The null move heuristic for pruning the search,which is extremely popular among many top pro-grams, is another example of the TRIZ inventiveprinciple. Its premise is allowing one side to movetwice, which is again illegal in chess. Moving con-secutively without improving one’s position isoften a good enough reason to prune the firstmove. When the null move heuristic is explainedto grandmasters, they recognize it in the way theyformulate plans.

Chess programs make use of killer move heuristics,which allow them to try “good moves” in differentpositions. This heuristic parallels GrandmasterGreenfeld’s proposal to try the same idea in differ-ent variations over and over again.

Tal’s “ice hockey concept” is implemented as aheuristic in Deep Junior’s evaluation function.Proximity to the opponent king is highly encour-aged. Some might recognize it in the effective tech-nique of influence maps.

Brainstorming is a common human techniquedesigned to generate a large number of ideas forsolving a problem. The idea of tossing out differenthypotheses sometimes at random is based uponthe hope that quantity might eventually lead toquality.

Evolutionary paradigms, such as genetic algo-rithms and genetic programming, follow the “quan-

tity leads to quality” idea. They have been a suc-cessful technique in many different fields. In chessthey are used to yield improved evaluation func-tions. The advantage of these “electronic brain-storming” methods is in the ability to obtain sev-eral good evaluation functions. The functionsobtained may be very different, allowing chess pro-grams to switch personalities.

Flexibility is innate in strong chess programsthat employ an adaptive evaluation model. Theirevaluation modifies scales and values according todifferent types of positions or to different phasesin the game—just like a chess player would knowthat in some positions pawns are worthless, whilein others a particular pawn may make a world ofdifference.

Attempts to address the difficult issue of “posi-tional sacrifice” were made throughout the historyof computer chess. Programs have been created

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Figure 2. Deep Fritz Versus Deep Junior [B67]. BGN World Quali-fiers, Cadaques (3), 25 April 2001. Position After 46 … Rxa2.

Kasparov singled out this position where Deep Junior began a beau-tiful combination. Deep Junior deliberately left its bishop en-pris.Deep Fritz took it and held a decisive material advantage of a fullpiece and a pawn. Deep Junior had to come up with a hat trick. Oth-erwise, it would lose. Interestingly enough, Deep Junior evaluated theposition as being about a pawn up for itself! The reason was that itsaw sufficient compensation in the white king’s poor safety.

47.Rxf2 Ra4 634kN/s -0.92/18 3 48.Qb3 Ra1+ 1335kN/s -0.93/1825 49.Kb2 Rh1 1558kN/s -1.05/19 9:37 50.Qg3 (Qa3) 50...Qa1+1477kN/s -1.23/18 2:34 51.Kb3 Rb1+ 1493kN/s -1.53/19 6:29 52.Kc4d5+ 286kN/s -1.83/19 11:37

with an element of speculation built into them.For example, in some positions, such programswould search for patterns that seemingly allow athematic maneuver. An example of such a positionis one that allows the “Greek gift” romantic pat-tern.

Upon pattern recognition, the program would sac-rifice a piece hoping that the outcome would workout for it just as it worked in past similar situations.This ad hoc method was certainly entertaining, butit failed miserably against strong computer oppo-nents who would not fall for the resulting compli-cations.

Vishy Anand, the reigning world chess champi-on, characterized Deep Junior as the program thattakes compensation to the limit. He referred toDeep Junior’s recognizable style as willing to takechances by sacrificing material without apparentgains. If humans played this way, they would beconsidered somewhere between unsound andcheeky.

Chess is an infinite game from the practicalsense. This means that every chess move carries acertain amount of risk. Accurately estimating therisk involved in a move is critical.

Deep Junior’s evaluation function is the productof a mathematical model for risk assessment andmachine learning. The program is trained usingthis model off the board by learning selected posi-tions from a game corpus. Initially, Deep Junior’straining set was compiled from games played byworld-class human players. Today, the training setconsists of positions played by computers only.

The example illustrated in figures 2–5 was point-ed out to me by Garry Kasparov. He received adatabase of Deep Junior games prior to our matchand studied them. The game that attracted hisattention was held at the BGN World Qualifiers, inCadaques, Spain, on 25 April 2001, between DeepFritz and Deep Junior 6.0. Kasparov remarked thatthe only way such a maneuver could be demon-strated by a human is if the human player had con-ceived of such a theme beforehand. He contendedthat this position only could occur artificially, byplaying it backwards. This is the work of chessproblemists who create chess compositions andnot a possible product of practical chess players.

Humans still prove to be superior over programsin some positions, in particular, a class of positionslabeled “fortresses,” recognizable by the longblocked pawn chains in them. Limited to theemployment of the search and evaluate paradigm,it would require an “infinite search horizon” toplay such positions. On the other hand, even low-skilled players can understand them quickly rely-ing on geometric vision. Sadly for humans, suchpositions are so rare that they are of little practicalimportance to the programmers. A notable excep-tion is the work on blockage detection in pawn

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Figure 3. Deep Fritz Versus Deep Junior. Position After 52 … Qxd5+.

Here came another pawn sacrifice. By the mere looks of the position, itseemed that black was running out of resources, desperately trying to createsomething. Paradoxically, by the increase in its evaluation of its position,Deep Junior was becoming more and more confident in its position.

53.exd5 Qa6+ 946kN/s -1.83/17 54 54.Kc3 Qa5+ 419kN/s -2.56/19 3 55.Kd3Rd1+ 808kN/s -2.78/19 3 56.Ke4 (Ke3) 56...Qxd5+ 934kN/s -3.67/17 31

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Figure 4. Deep Fritz Versus Deep Junior. Position After 56 … Qxd5+.

endgames (Tabibi, Felner, and Netanyahu 2004),which copes with such positions and was integrat-ed into the Israeli Falcon program.

Creative SynergyAt the start of this new millennium extremelystrong chess programs were made available to any-one. As a result chess is undergoing a small revolu-tion. True, there are no new openings beinginvented, but this was also true for more than 50years BC (before computers).

Modern chess has become more concrete andaccurate. Chess programs allowed the revival of oldopenings that were deserted because they werebelieved to be inferior. Openings, such as the Jan-ish gambit in the Ruy Lopez, that were “lyingdead” for decades are now extremely popularthanks to computer analysis.

Computers have inspired “concrete chess,” allow-

ing players to desert “old Russian school principles,”a discipline popular during Karpov’s era. Chess thenwas a dogmatic game, where one expected to bepunished for not following the principles.

In some openings, for example, computers nowallow one to develop the queen early. Computersproved that she could grab a few pawns and bailout alive. Such a maneuver is marked as a big “no-no” even in recent chess literature just because it istrue most of the time.

Computers managed to cast doubt on the wayhumans used to play chess. In this sense, they haveencouraged creativity among humans.

To quote world champion Vishy Anand, “Iwould say nowadays it is impossible to work with-out computers. And you don’t become mechanicalat all. It allows you to do incredibly creative things.I mean there are positions I can work on where itwas not feasible to work on alone…. I would alsosay we have developed a certain tolerance for

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Figure 5. Deep Fritz Versus Deep Junior. Winning Queen Maneuver.

At this point, Deep Junior had a winning score. Its slick winningqueen maneuver captured Kasparov’s eye: The black queen tookadvantage of the trapped king position (having only two free squarese4 and e3) to execute a stairways queen maneuver. The queen’s objec-tive was to reach the a3 square, the only square allowing her a dead-ly horizontal check.

57.Ke3 Qc5+ 1167kN/s -3.67/17 29 58.Kf3 Qc6+ 1361kN/s -3.67/19 3:29 59.Ke3 Qb6+ 1261kN/s -3.67/16 35 60.Ke4 Qb7+1215kN/s -3.67/19 3 61.Ke3 Qa7+ 1174kN/s -3.67/17 24 62.Kf3Qa8+ 641kN/s -3.82/19 3 63.Ke3 Qa3+ 1224kN/s -3.82/17 3464.Ke4

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Figure 6. Deep Fritz Versus Deep Junior. Position After 64. Ke4.

Now came the point of the combination, which was the rook’s checkon d4. It forced Deep Fritz’s knight to abandon its protection of thequeen. Deep Junior managed to turn the tables, obtaining a decisivematerial advantage.

Rd4+ 929kN/s -3.97/19 3 65.Nxd4 Qxg3 1243kN/s -4.15/19 1:2466.Nc6+ Kd6 605kN/s -4.75/20 3 67.Nxe5 (Rf1) 67...fxe5 1300kN/s-5.35/19 5:32 68.Rd2+ (Rf3) 68...Kc6 1252kN/s -5.35/18 1:1769.Rd8 (c3) 69...Qf4+ 1432kN/s -5.35/15 1:01 0-1

unusual moves. I mean, humans themselves playunusual moves nowadays. When I see some movemy first reaction is no longer ‘Oh, this is ghastly.’My first reaction is ‘aha, the tactics are working’ orsomething. So I would say it is an evolutionarything. We have slowly learned that our under-standing of chess was not complete and computershave got better.”1

As a by-product of the computer revolution,many young chess players, who grew up in the ageof personal computer chess, infiltrated the top 20.Notable examples are grandmasters MagnusCarlsen and Sergei Karjakin, who are still under 20but are singled out as candidates for becoming thenext world chess champion.

There are also very good young poker players.The Internet made the opportunity to study thegame available to all. Before computers, one had ahard time finding high-quality competition, whichis essential to improve. Imagine what would hap-pen to basketball if everyone had a chance to playwith NBA quality players. This may be possible inthe future, when robots might play basketball. Inthat case, perhaps the game of basketball willundergo a revolution as well.

Prior to the computer age, players had to movewith seconds (assistants). Now all top players movearound with their laptops. They use them to findinnovative moves that will surprise their oppo-nents mostly based on computer evaluation. Theolder generation of chess players finds it hard tocompete against the computer-age players—“forthem it is like confronting tennis players with ahuge serve,” explains Grandmaster Alterman .

During the late 1990s, a group of strong Dutchplayers did develop an effective method to con-front AI. Paradoxically, their method, dubbed“anticomputer chess,” was to dilute as much cre-ativity as possible out of the position. “Anticom-puter chess” is about simplifying the position bylimiting the search branching factor from the AIperspective. Kasparov, aware of the method,refrained from using it against Deep Junior—it wasagainst his character to become anticreative.

ConclusionsComputers today are more creative than humansin the game of chess. This article outlined fourmain reasons supporting the above claim.

Creative chess play is mainly about willingnessto take risks by deviating from the conventionalmoves. It is evident that there is correlationbetween strength of play and creativity (Garry Kas-parov). In this sense, computers know how to cal-culate risks in chess better than humans. In addi-tion, they simply are more objective whenanalyzing a position. As a result of Deep Junior’sexperience in playing humans, we have often

found that human opponents bring a certainamount of prejudice when assessing their position.

Humans are more predictable than computers intheir game (a fact reflected by their higher drawrate). This is mainly because humans tend to dis-qualify moves that seem to be against the princi-ples of playing the game. However computersprove that such moves can work for concrete rea-sons. For the past decade, the only successful wayfor humans to confront computers was to limittheir creativity by “drying up the position” andsimplifying it. Our machine-learning mechanismsthat benefited in the past by analyzing humangames now only can benefit by analyzing comput-er games.

Computer chess programs have changed theway people play chess. They rely more and moreon creative ideas that are cooked using their com-puters. Perhaps they tell us something more aboutthe nature of the game. If concrete play works well,it is because chess is more a tactical game than astrategic one. Sadly enough, it looks as if thehuman race has given up too early. Ever since Gar-ry Kasparov’s courageous effort to confront com-puters, which lasted over a decade, there is no suc-cessor who has attempted confronting AI in aserious manner.

My hope is that, among those young emergingchess giants, there will be found one who is cre-ative enough and familiar with the best chess pro-grams and who will dare to challenge AI domi-nance over the game of chess.

Note1. See the interview with Anand by Sriram Srinivasan andJaideep Unudurti on intuition, creativity, and blitz chessmade 26 December 2008 at www.chessbase.com/newsde-tail.asp?newsid=5282.

ReferencesAltshuller, G. 1973. Innovation Algorithm. Worcester, MA:Technical Innovation Center.

Altshuller, G. 1984. Creativity as an Exact Science. NewYork: Gordon & Breach.

Amatzia, A. 1998. Creative Chess. London: EverymanChess.

De Bono, E. 1970. Lateral Thinking: Creativity Step by Step.New York: Harper & Row.

Kelly, K. 1995. Out of Control: The New Biology of Machines,Social Systems, and the Economic World. New York: BasicBooks

Tabibi, O. D.; Felner, A.; and Netanyahu, N. S. 2004.Blockage Detection in King and Pawn Endings. Interna-tional Computer Games Association Journal 27(3) (Septem-ber).

Shay Bushinsky is the coauthor of Deep Junior, a five-time world computer chess champion. Bushinsky cur-rently teaches AI at the universities of Tel-Aviv and Haifain Israel.

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