such higher-level chunks. Even though heuristic rules are not rigorous in
the way that the official rules are, they provide shortcut insights into what is
going on on the board, which knowledge of the official rules does not. This
much was recognized from the start; it was simply underestimated how
large a role the intuitive, chunked understanding of the chess world plays
in human chess skill. It was predicted that a program having some basic
heuristics, coupled with the blinding speed and accuracy of a computer to
look ahead in the game and analyze each possible move, would easily beat
top-flight human players-a prediction which, even after twenty-five years
of intense work by various people, still is far from being realized.
People are nowadays tackling the chess problem from various angles.
One of the most novel involves the hypothesis that looking ahead is a silly
thing to do. One should instead merely look at what is on the board at
present, and, using some heuristics, generate a plan, and then find a move
which advances that particular plan. Of course, rules for the formulation of
chess plans will necessarily involve heuristics which are, in some sense,
"flattened" versions of looking ahead. That is, the equivalent of many
games' experience of looking ahead is "squeezed" into another form which
ostensibly doesn't involve looking ahead. In some sense this is a game of
words. But if the "flattened" knowledge gives answers more efficiently than
the actual look-ahead-even if it occasionally misleads-then something
has been gained. Now this kind of distillation of knowledge into more
highly usable forms is just what intelligence excels at-so look-ahead-Iess
chess is probably a fruitful line of research to push. Particularly intriguing
would be to devise a program which itself could convert knowledge gained
from looking ahead into "flattened" rules-but that is an immense task.
Samuel's Checker ProgramAs a matter of fact, such a method was developed by Arthur Samuel in his
admirable checker-playing program. Samuel's trick was to use both dynamic
(look-ahead) and static (no-look-ahead) ways of evaluating any given board
position. The static method involved a simple mathematical function of
several quantities characterizing any board position, and thus could be
calculated practically instantaneously, whereas the dynamic evaluation
method involved creating a "tree" of possible future moves, responses to
them, responses to the responses, and so forth (as was shown in Fig. 38). In
the static evaluation function there were some parameters which could
vary; the effect of varying them was to provide a set of different possible
versions of the static evaluation function. Samuel's strategy was to select, in
an evolutionary way, better and better values of those parameters.
Here's how this was done: each time time the program evaluated a
board position, it did so both statically and dynamically. The answer gotten
by looking ahead-let us call it D-wa~ used in determining the move to be
made. The purpose of S, the static evaluation, was trickier: on each move,
the variable parameters were readjusted slightly so that S approximated D(^604) Artificial Intelligence: Retrospects