2. Philosophy - Stefano Franchi

C OMBINATORIAL EXPLOSIONS
the difficulty of practically finding the “solution” of chess whose existence was guaranteed
by the fundamental minimax theorem. Such a solution would be what we may call an “op-
timal” solution: it would provided a “strategy” that assures the chess player either of a win
or a tie, not of a method to play passably good chess. Therefore, he was not interested in a
chess-playing computer since he was trying to find chess-solving algorithms that a machine
could use. 27
As a consequence of von Neumann’s shifting interests, chess (and games) were
brought into the orbit of computer science, or rather into the orbit of computers, while being
understood within the conceptual framework of game theory. The “optimizing” perspective
of the theory, however, still dominates the theoretical agenda: the researchers were after a
solution in terms of a minimaxing algorithm and the task seemed impossibly prohibitive,
even on a fast computer. Even more importantly, there need not be any direct link between
the algorithm being used and how the game is actually played. In fact, this last aspect is
totally irrelevant as it is irrelevant to the automatic solution of differential equations that
human beings normally do not use numerical algorithms when they play the game of alge-
bra. The two conceptual steps that bridge this gap are taken in rapid succession by Turing
and Shannon and then by Simon and Newell in the early 1950s. It is their work that closes
the circle and allows chess to become as productive for Artificial Intelligence as Drosophila
had been for genetics.
In a few years span, Alan Turing and Claude Shannon publish two papers describing
the basic design principles that would allow a computer to play chess, although they never
come to write the actual programs. However, their use of the game is still outside Artificial
Intelligence, broadly construed: they want to prove, by demonstrating the practical possi-
bility of a chess-playing machine, that computers are able to manipulate symbols and not
just number-crunchers perennially devoted to solving differential equations as most people,
at the time, took them to be. The quality of the game played by a machine, its intelligence,
so to speak, was thus less important than the demonstration that such a machine could ex-
27. To put it differently, he was exploiting the connection between game-theory and linear programming
that was beginning to consolidate around 1950s.
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