AI - a Guide to Intelligent Systems.pdf - Member of EEPIS

84 UNCERTAINTY MANAGEMENT IN RULE-BASED EXPERT SYSTEMS
Bayesian reasoning and certainty factors, and determined appropriate areas for
their applications.
The most important lessons learned in this chapter are:
. Uncertainty is the lack of exact knowledge that would allow us to reach a
perfectly reliable conclusion. The main sources of uncertain knowledge in
expert systems are: weak implications, imprecise language, missing data and
combining the views of different experts.
. Probability theory provides an exact, mathematically correct, approach to
uncertainty management in expert systems. The Bayesian rule permits us
to determine the probability of a hypothesis given that some evidence has
been observed.
. PROSPECTOR, an expert system for mineral exploration, was the first
successful system to employ Bayesian rules of evidence to propagate uncertainties
throughout the system.
. In the Bayesian approach, an expert is required to provide the prior
probability of hypothesis H and values for the likelihood of sufficiency, LS,
to measure belief in the hypothesis if evidence E is present, and the likelihood
of necessity, LN, to measure disbelief in hypothesis H if the same evidence is
missing. The Bayesian method uses rules of the following form:
IF
THEN
E is true {LS, LN}
H is true {prior probability}
. To employ the Bayesian approach, we must satisfy the conditional independence
of evidence. We also should have reliable statistical data and define the
prior probabilities for each hypothesis. As these requirements are rarely
satisfied in real-world problems, only a few systems have been built based
on Bayesian reasoning.
. Certainty factors theory is a popular alternative to Bayesian reasoning. The
basic principles of this theory were introduced in MYCIN, a diagnostic
medical expert system.
. Certainty factors theory provides a judgemental approach to uncertainty
management in expert systems. An expert is required to provide a certainty
factor, cf, to represent the level of belief in hypothesis H given that evidence E
has been observed. The certainty factors method uses rules of the following
form:
IF
THEN
E is true
H is true {cf}
. Certainty factors are used if the probabilities are not known or cannot be
easily obtained. Certainty theory can manage incrementally acquired
evidence, the conjunction and disjunction of hypotheses, as well as evidences
with different degrees of belief.