Correction Appended

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The result is a densely proliferating tree of possibilities with the branches and twigs representing all the different ways the game could unfold. Looking ahead just seven plies (14 individual chess moves) requires examining 3514 (more than a billion trillion) leaves representing all the various outcomes.

As the computer tries to look deeper, the number of possibilities explodes. Programmers have learned clever ways to ''prune'' the trees, so that all but a fraction of the paths can be discarded without plumbing them all the way to the bottom. Even so, a chess-playing computer looking ahead seven plies might consider as many as 50 or 60 billion scenarios each time its turn comes around.

As bad as that sounds, in Go the situation is drastically worse. The tree of possible moves is so broad and dense that not even the fastest computer can negotiate it. The first player can put a stone in any of 361 places; the opponent can respond by placing a stone on any of 360 places, and so on. As the game continues, there are steadily fewer possible places to play. But, on average, a player is faced with about 200 possible moves, compared with just 35 in chess.

As a computer scientist would put it, the branching factor is much higher for Go than for chess. In chess the approximate number of possible board positions after only four moves is typically 35x35x35x35= 1,500,625. For Go, the number is 200x200x200x200=1,600,000,000 -- and far more toward the beginning of a game. Search one ply deeper and the numbers rapidly diverge: about 1.8 billion possible outcomes for chess and 64 trillion for Go.

Looking ahead 14 moves, or seven plies, in Go creates a search tree not with a mere 3514 leaves, as for chess, but with more than 20014 leaves. Pruning techniques cut this down to about ten thousand trillion possibilities to consider. Still, a Go computer as fast as Deep Blue (which analyzed some 200 million chess positions per second) would take a year and a half to mull over a single move.

Even worse, performing so laborious a search would give the computer no significant advantage over its human opponent. After sifting through the myriad possibilities, a chess-playing computer tries to choose the move that will leave it in the strongest position. It determines this by using fairly simple formulas called evaluation functions. Each piece can be assigned a number indicating its rank (pawns are worth 1, knights and bishops 3, rooks 5, queens 9). This figure can be multiplied by another number indicating the strength of the piece's position on the board. Other formulas quantify concepts like ''king safety,'' or how wellprotected that piece is. These rules, called heuristics, are hardly infallible, but they give the computer a rough sense of the state of the game and a basis on which to make its decisions.

Go does not succumb to such simple analysis. There is no single piece, like a king, whose loss decides the game. Even counting the amount of territory each player has captured is not very revealing. With the placement of a single stone, a seeming underdog might surround the grand structure his opponent has been assiduously building and turn it -- jujitsu-like -- into his own. ''You're stringing all these stones together, and if you don't watch out the whole collection becomes dinner for your opponent,'' Mr. Klinger said.

Expert Go players evaluate the state of the board by using their skills at pattern recognition, and these are very hard to capture in an algorithm. After years of experience, they can look at a complex configuration and sense whether it is ''alive,'' meaning that it is constructed in such a way that it cannot be captured, or ''dead,'' so that no amount of reinforcement can save it. Learning to sense life and death is crucial. A player does not want to waste stones attacking a group that is invulnerable, or defending one that is doomed. Sometimes there are fairly obvious clues: if a group of stones contains two configurations called eyes, it can fend off any attempt to capture it. But often the difference between life and death is difficult to perceive, hinging on a single stone.

Go masters can also sense whether several unconnected stones might be slowly joined to form a group, or whether two smaller groups might be combined into a larger, stronger whole.

To get a computer to do this kind of analysis, programmers must confront fundamental problems in artificial intelligence. Mr. Fotland armed his program, The Many Faces of Go, with basic concepts like territory and connectivity (whether several stones are in adjacent positions). It can also recognize some 1,100 different patterns, each of which sets off a sequence of suggested moves, and it has access to about 200 higher-level strategic notions like ''attack a weak group'' or ''expand into a potential territory'' or ''if behind, make unreasonable invasions that you don't expect to work.'' Like Deep Blue, the program draws on a library of standard openings and other commonly used plays. Drawing on this knowledge, it will consider only about 5 or 10 of the approximately 200 possible moves available to it in a typical turn.

But programming this kind of knowledge is extremely difficult. ''People are so good at dealing with fuzzy concepts,'' said David Mechner, a doctoral student in neural science at New York University who is a top-ranked amateur Go player. But how do you tell a computer that several stones might end up being connected, but not necessarily? Mr. Mechner and Mr. Klinger are studying these kinds of problems and fine-tuning an algorithm for recognizing life and death. They hope to soon join the handful of programmers competing to make the best Go program.

The winner of the FOST Cup, sponsored by the Japanese Fusion of Science and Technology organization and held in Nagoya next month as part of the International Joint Conference on Artificial Intelligence, will get about $17,000. The contest for the $7,000 Ing Cup, sponsored by the Ing Chang-ki Wei-Chi Educational Foundation in Taipei, will be held in November in the San Francisco Bay Area. (The winner will have the opportunity to challenge three young Go players for additional prizes).

But winning the $1.4 million prize promised by the Ing foundation to a program that beats a human champion may be an impossible dream. The offer expires in the year 2000. Go programmers are hoping it will be extended for another century or two.

Photos: Showing the human face of go, David Mechner and Janice Kim play on a traditional Japanese board, sitting on tatami mats with shoji screens behind them; for many reasons, computers are not as comfortable with the game. (The New York Times; Photo by Linda Rosier; Illustration by Juan Velasco)(pg. C1); Janice Kim, right, professional Go player and software consultant, plays David Mechner, software developer. (Linda Rosier for The New York Times)(pg. C2) Charts: ''Mind Vs. Machine: The Game of Go'' Go is an apparently simple game in which players place black or white pieces on the board one at a time, with the goal of encircling (and then removing) the other player's stones. At each move, a multiplicity of possiblities exists. Experts say powerful computers can have trouble recognizing patterns of play that human players find easy to see. ''Seeing Subtle Patterns'' In this hypothetical game, the corner positions are very similar, but the results very different: the black stones in the lower right corner are ''dead,'' about to be hemmed in; those in the upper left are ''alive.'' Recognizing this is relatively easy for people, relatively hard for computers. ''Too Many Possiblities'' Even after 30 moves in this typical game, the number of possible continuations is overwhelming, with 331 possible ''legal'' moves for black. A nave computer might consider all of them and their myriad continuations, but an adept human player would choose from far fewer ''reasonable'' choices. ''Where Do They Stand?'' In this actual tournament game, after 63 moves, most computer Go programs, but not most experienced players, would have difficulty answering these questions about the positions: Is group ''a'' dead or alive? What about group ''b'' and the single stone marked ''c''? $(pg. C1) Answers on page C2. (Sources: Tim Klinger and Janice Kim) ''Where They Stand'' Answers to questions about the third game on page C1: An experienced Go player would say the group of white stones marked ''a'' is dead, because it is vulnerable to being encircled as more black stones are played. The block of black stones labeled ''b'' is alive, with the potential to protect itself from encirclement. The white stone labeled ''c'' is dead. (pg. C2)