flex Expert System Toolkit - LPIS

Appendix D - Dealing with Uncertainty 226
Appendix D - Dealing with Uncertainty
This document aims to serve as a basic introduction to the various
techniques that Flex now offers to support uncertainty. For a more detailed
explanation, you are referred to:
“Knowledge-Based Systems for Engineers and Scientists“
( Hopgood, CRC Press, ISBN: 0-8493-8616-0 )
Traditional expert systems work on the basis that everything is either true or
false, and that any rule whose conditions are satisfiable is useable, i.e. its
conclusion(s) are true. This is rather simplistic and can lead to quite brittle
expert systems. Flex now offers support for where the domain knowledge is
not so clearcut.
Given a rule:
rule1: if A & B then C
there are 3 potential areas for uncertainty.
- Uncertainty in data (how true are A and B)
- Uncertainty in the rule (how often does A and B imply C)
- Impreciseness in general
The first 2 can be handled using probabilities and the third using fuzzy logic.
Uncertainty in Data
Combining Probabilities
A probabilistic rule in flex can look like a production rule:
uncertainty_rule r33
if the temperature is high
and the water_level is not low
then the pressure is high .
Later on we shall see that this is equivalent to:
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