The Artificial Intelligence community sought to understand

human intelligence by building computer programs, which

exhibited intelligent behavior. Intelligence was perceived to

be a problem solving ability. Most human problems appeared to

have reasoned, rather than mathematical, solutions. The

diagnosis of a disease could hardly be calculated. If a patient

had a group of symptoms, then she had a particular disease. But,

such reasoning required prior knowledge. The programs needed to

have the “knowledge” that the disease exhibited a particular

group of symptoms. For the AI community, that vague knowledge

residing in the minds of “Experts” was superior to text book

knowledge. So they called the programs, which solved such

problems, Expert Systems.

Expert Systems managed goal oriented problem solving tasks

including diagnosis, planning, scheduling, configuration and

design. One method of knowledge representation was through “If,

then...” rules. When the “If” part of a rule was satisfied, then

the “Then” part of the rule was concluded. These became rule

based Expert Systems. But knowledge was sometimes factual and

at other times, vague. Factual knowledge had clear cause to

effect relationships, where clear conclusions could be drawn

from concrete rules. Pain was one symptom of a disease. If the

disease always exhibited pain, then pain pointed to the

disease. But vague and judgmental knowledge was called

heuristic knowledge. It was more of an art. The pain symptom

could not mechanically point to diseases, which occasionally

exhibited pain. Uncertainty did not yield concrete answers.

The AI community tried to solve this problem by suggesting a

statistical, or heuristic analysis of uncertainty. The

possibilities were represented by real numbers or by sets of

real-valued vectors. The vectors were evaluated by means of

different “fuzzy” concepts. The components of the measurements

were listed, giving the basis of the numerical values.

Variations were combined, using methods for computing

combination of variances. The combined uncertainty and its

components were expressed in the form of “standard deviations.”

Uncertainty was given a mathematical expression, which was

hardly useful in the diagnosis of a disease.

The human mind did not compute mathematical relationships to

assess uncertainty. The mind knew that a particular symptom

pointed to a possibility, because it used intuition, a process

of elimination, to instantly identify patterns. Vague

information was powerfully useful to an elimination process,

since they eliminated many other possibilities. If the patient

lacked pain, all diseases, which always exhibited pain, could

be eliminated. Diseases, which sometimes exhibited pain were

retained. Further symptoms helped identification from a greatly

reduced database. A selection was easier from a smaller group.

Uncertainty could be powerfully useful for an elimination

process.

Intuition was an algorithm, which evaluated the whole database,

eliminating every context that did not fit. This algorithm has

powered Expert Systems which acted speedily to recognize a

disease, identify a case law or diagnose the problems of a

complex machine. It was instant, holistic, and logical. If

several parallel answers could be presented, as in the multiple

parameters of a power plant, recognition was instant. For the

mind, where millions of parameters were simultaneously

presented, real time pattern recognition was practical. And

elimination was the key, which could conclusively handle

uncertainty, without resort to abstruse calculations.