Can Power Expert Systems
Uncertainty -A Real World Problem
Uncertainty can powerfully assist Expert Systems to diagnose problems by ignoring the “If, then..” routine and by following an elimination algorithm. This website suggests that intuition of the human mind follows such a routine. From its early beginnings, the Artificial Intelligence community sought to understand human intelligence by building computer programs, which exhibit intelligent behavior. AI researchers assume rightly that intelligence is essentially a problem solving ability. But, they make the mistake of assuming that such problems have clear logical, or mathematical solutions. Unfortunately for them, problems in the real world do not present clear cut answers. The diagnosis of a disease does not follow simple logical paths; nor can it be calculated. If a patient has a group of symptoms, then she has a particular disease. But many of these symptoms are shared by other diseases. Occasionally, some of these symptoms may also be absent for the disease. The real world presents the problem of uncertainty.
- In an application of artificial
intelligence, Expert Systems manage goal oriented problem solving
tasks by using the logic in the minds of experts in these fields.
- One method is to search the knowledge
base through "If, then..." rules.
- But such searches paths increase
exponentially, as the size of the knowledge base increases.
- But, quick solutions are feasible, if
the logic is used to eliminate unsuitable data from the entire
- Using "fuzzy" logic concepts
lead to unsatisfactory solutions.
- The advantage of using elimination is that "uncertain" elements can be left in the knowledge base to be subsequently
eliminated through "certain" considerations.
- The elimination logic has proved to be
effective in diagnostics.
Uncertainty -Expert Systems
AI community was faced with the problem of ambiguity. In many fields,
knowledge was found to be vague. Only the specialist knew when a
particular symptom was applicable. There were experts in each field,
with extensive prior knowledge. The AI objective was to transfer the
logic of the decisions in the minds of experts into diagnostic
programs. Expert Systems were expected to yield intelligent answers
to the problems in the world. Expert Systems managed goal oriented
problem solving tasks including diagnosis, planning, scheduling,
configuration and design. In every case, the AI community avoided the
blurred boundaries of problems.
Uncertainty - Rule
based Expert Systems
Systems operated on certainties. 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. If a particular symptom was present, then a specific
disease could be selected. But knowledge was sometimes factual and at
other times, vague. Factual knowledge had clear cause to effect
relationships, where 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 presented a problem, when pain was present only
occasionally. Such ambiguity was useless information for rule based
Expert Systems. Many diseases also shared many symptoms. So, Expert
Systems conducted back and forth searches, till they located a
particular disease, which presented all the symptoms.
Uncertainty - The
Exponential Growth Problem
back and forth searches of the Expert Systems faced problems with
large databases. In theory, the search process took twice the time
for each newly added symptom. If a search for a single symptom takes
1 second, 2 symptoms require 2 seconds and 12 symptoms will take more
than 4 HOURS! Any doctor can evaluate thousands of symptoms within
milliseconds. But this problem can be solved if the whole database is
evaluated on an elimination basis. Any diseases, which are clearly
unrelated to the presence, or absence of particular symptoms are
eliminated from the database. If a symptom is present, all diseases,
which positively do not present the symptom are eliminated. If a
symptom is absent, all diseases, which positively present the symptom
are eliminated. This process can swiftly evaluate any reasonable
database and effectively handle uncertainty.
Uncertainty - Elimination
Benefits From Uncertainty
AI community tried to solve the ambiguity 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.
Instead of delivering a mathematical formula as diagnosis, ambiguity
can become powerfully useful information for other members of the
database. Pain may be only occasionally present for some diseases,
but will be certain for some and completely absent for many others.
The search process eliminates the "certain" diseases. Only
an elimination routine can take advantage of uncertainty.
Uncertainty - Intuition
Uses An Elimination Process
human mind does not compute mathematical relationships to solve
problems. The mind knows that a particular symptom points to a
possibility, because it uses intuition,
a process of elimination, to instantly identify patterns. Vague
information is powerfully useful to an elimination process, since it
eliminates myriad other possibilities. If the patient lacks pain, all
diseases, which always exhibit pain, are eliminated. Diseases, which
sometimes exhibit pain are retained. Further symptoms help
identification from a greatly reduced database. The final choice is
easier from a smaller group. Uncertainty can be powerfully useful for
an elimination process.
Uncertainty - Elimination - A Successful Expert System
Freeman, the famous neurobiologist outlined the process: “The
cognitive guys think it's just impossible to keep throwing everything
you've got into the computation every time. But, that is exactly what
the brain does. Consciousness is about bringing your entire history
to bear on your next step, your next breath, your next moment.” An
elimination algorithm can evaluate the whole database, eliminating
every context which does not fit. This algorithm has been proved. It
has powered Expert Systems which speedily recognize a disease,
identify a case law or diagnose the problems of a complex machine.
These systems are swift, holistic, and logical. If several parallel
answers are presented, as in the multiple parameters of a power
plant, recognition can even be instant. For the mind, where millions
of parameters are simultaneously presented, real time pattern
recognition is practical. And elimination is the key, which can
conclusively handle uncertainty, without delivering a perplexing
statistical calculations as the diagnosis.
This page was last updated on 12-Sep-2016.