Expert Systems: Uncertainty Management

Question 1

Examples of a source of uncertainty

Answer 1

Weak implications, Imprecise language, Unknown data, Combining the views of different experts

Question 2

The difference between probability and fuzzy membership in measuring uncertainty

Answer 2

The probability of an event is the proportion of cases in which the event occurs. It’s a scientific measure of chance. It can be expressed mathematically as a numerical index with a range between 0 to unity. Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on the degree of membership rather than on crisp membership of classical binary logic

Question 3

What is uncertainty in AI?

Answer 3

The lack of exact knowledge that would enable us to reach a perfectly reliable solution

Question 4

Weak implications as a source of uncertainty

Answer 4

They are vague associations between IF and THEN parts of the rules.

Question 5

Examples of weak implications

Answer 5

If there are any certainty factors to indicate a degree of correlation or if there are any further reasoning based on the fact stated

Question 6

Imprecise language as a source of uncertainty

Answer 6

There are various ways to express knowledge in the precise IF-THEN form of rules

Question 7

Unknown data as a source of uncertainty

Answer 7

When data are incomplete or missing, the only solution is to accept the value “unknown” and proceed to an approximate reasoning with this value

Question 8

Combining views as a source of uncertainty

Answer 8

Experts often have contradictory opinions and produce conflicting rules. To resolve the conflict, one has to attach a weight to each expert and then calculate the composite conclusion. No systematic method exists to obtain weights. Experts will reach exactly the same conclusions

Question 9

How to calculate the probability of success?

Answer 9

The number of successes / The number of possible outcomes

Question 10

How to calculate the probability of failure?

Answer 10

The number of failures / The number of possible outcomes

Question 11

Crisp set (logic) vs Fuzzy set (logic)

Answer 11

Crisp set defines value either 1 or 0. It is also called a classical set and it shows full membership meaning true/false, yes/no, 0/1. Fuzzy set defines values between 0 and 1. It specifies the degree to which something is true and it shows partial membership meaning true to false, yes to no, 0 to 1