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.
About the author:
Abraham Thomas is the author of The Intuitive Algorithm, a book,
which suggests that intuition is a pattern recognition
algorithm. The ebook version is available at
http://www.intuition.co.in. The book may be purchased only in
India. The website, provides a free movie and a walk through to
explain the ideas.