knowledge engineering Examples taken from life insurance expert:

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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RULE 1 if age is old and gender is male and smoker is no then risk is low; RULE 2 if age is middle and gender is male and smoker is yes then risk is high; RULE 3 if age is young and gender is female and smoker is no then risk is low;

knowledge engineering
• Examples taken from life insurance expert:
Example
Age
Smoker?
Gender
Risk
1
Young
No
Female
Low
2
Middle
Yes
Male
High
3
Old
No
Male
Low
knowledge engineering
• The following rules show how the induction
technique generates general rules from specific
examples:
RULE 1 if age is old
and gender is male
and smoker is no
then risk is low;
RULE 2 if age is middle
and gender is male
and smoker is yes
then risk is high:
Small Expert System Project
• Consider the problem taken from life insurance
expert (slides 25&26). Write a complete
PROLOG program that could support the
decision of a life insurance expert. Add
necessary facts and rules to produce a well
designed knowledge base.
Transcribed Image Text:knowledge engineering • Examples taken from life insurance expert: Example Age Smoker? Gender Risk 1 Young No Female Low 2 Middle Yes Male High 3 Old No Male Low knowledge engineering • The following rules show how the induction technique generates general rules from specific examples: RULE 1 if age is old and gender is male and smoker is no then risk is low; RULE 2 if age is middle and gender is male and smoker is yes then risk is high: Small Expert System Project • Consider the problem taken from life insurance expert (slides 25&26). Write a complete PROLOG program that could support the decision of a life insurance expert. Add necessary facts and rules to produce a well designed knowledge base.
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