Feigenbaum wasn`t as interested in the chemistry per se as he was in how a scientist develops a hypothesis to explain unprecedented data, in other words, how a scientist thinks--and, by extension, how a machine could be taught to think. To duplicate that process Feigenbaum had to program in all the ``hard`` knowledge about chemistry a chemist with a Ph.D. would know, plus the far less quantifiable knowledge about how the scientist proceeds to make decisions when he or she isn`t really sure or the data seem ambiguous.
It rapidly became clear that acquiring this second sort of knowledge--the ``soft`` knowledge--was the bottleneck for AI research. ``Essentially we are miners,`` Feigenbaum has said. ``We extract the gemstones of knowledge that are the private reserve of expert practitioners in each field.``
The knowledge of an expert is largely rule of thumb. Coming from experience and often the result of hunches, good guesses, intuition and creative leaps of the mind, it includes items the expert may not be able to explain logically. An everyday example of this kind of knowledge is the rule of thumb about warming up the car first before driving away on a cold morning. Most car owners may not understand precisely what warming up does to the internal-combustion engine, but they know from experience that it works. This kind of knowledge is also called ``heuristic,`` from the Greek heuriskein, to discover, the same root as for ``eureka.``
Knowledge engineering, an entirely new Ph.D. specialty, has grown out of the attempt to create expert systems. Knowledge engineers ask all sorts of what-if questions, attempting to foresee any and every problem a machine will be asked to tackle. Then they try to understand the human expert`s method of thinking through the problems and to translate each of these separate pieces of information into a symbol a computer can recognize and process with extraordinary speed.
Most expert systems are based on if-then rules: If this is true, then that is also true; if A, B and C are true, then D is also true. Given the complexities of first reducing these pieces of information to symbols that can be encoded in the zeros and ones of the binary system, the basic on-off switches of every digital computer, expert systems require immense amounts of computer coding and powerful machines to run them.
An example that recurs again and again in AI history is chess. Even before the modern-day computer was realized, mathematicians had theorized about and tried to build machines to play chess. Because there are a fixed number of chess pieces and a similarly limited number of legal moves, there is a finite number of possible chess plays. A digital computer can faithfully slog along, searching one by one through all these moves in response to its human opponent`s play. The computer might win by attrition--its human opponent would likely die before the game was over. By examining every possible move, however, the computer would always come up with the best move; a human could come up with a very effective move, but not always the one best move for a given situation.
Computers have been playing challenging chess for a decade or more now because scientists have learned varying systems of organizing effective strategies much as chess masters do, abandoning less likely directions of search, or ``pruning the search tree,`` as AI researchers describe it. (Going back to everyday heuristics, if on a below-zero morning your car`s engine doesn`t turn over, you don`t respond by checking the tires or the windshield wipers; you have pruned the search tree.)
DENDRAL performs so well it is now used by chemists all over the world and has spawned other expert systems. The methodology used to pick an expert`s brain and transform the results into computer code could be used to develop other expert-level inference systems, and in the early 1970s doctors at the Stanford Medical School began work on a computer program that would attempt to advise physicians on antibiotic selection for infectious diseases.
Edward Shortliffe, the young physician directing this project, met once a week with Stanley Cohen, the physician who later became famous as the codeveloper of the techniques for recombinant-DNA research, and another doctor, Stanton Axline. ``We sat around, went over patient cases and tried to understand how Axline and Cohen would decide to treat those cases,``
Shortliffe has said. ``We`d stop them--those of us who knew little medicine and were more computer scientists--and ask, `Well, why do you say that?` ``
In the week between sessions, Shortliffe would put the two doctors` rules of thumb into his emerging computer program, then ``we`d all have a good laugh the following week when I would show them how the computer had tried to handle the same case. What we did was discover the great simplifications they made in explaining the rules from the previous week, where they`d go wrong if you tried to run it on a different case.``